Large-Scale Analysis of Apolipoprotein CIII Glycosylation by Ultrahigh Resolution Mass Spectrometry.

Apolipoprotein-CIII (apo-CIII) is a glycoprotein involved in lipid metabolism and its levels are associated with cardiovascular disease risk. Apo-CIII sialylation is associated with improved plasma triglyceride levels and its glycosylation may have an effect on the clearance of triglyceride-rich lipoproteins by directing these particles to different metabolic pathways. Large-scale sample cohort studies are required to fully elucidate the role of apo-CIII glycosylation in lipid metabolism and associated cardiovascular disease. In this study, we revisited a high-throughput workflow for the analysis of intact apo-CIII by ultrahigh-resolution MALDI FT-ICR MS. The workflow includes a chemical oxidation step to reduce methionine oxidation heterogeneity and spectrum complexity. Sinapinic acid matrix was used to minimize the loss of sialic acids upon MALDI. MassyTools software was used to standardize and automate MS data processing and quality control. This method was applied on 771 plasma samples from individuals without diabetes allowing for an evaluation of the expression levels of apo-CIII glycoforms against a panel of lipid biomarkers demonstrating the validity of the method. Our study supports the hypothesis that triglyceride clearance may be regulated, or at least strongly influenced by apo-CIII sialylation. Interestingly, the association of apo-CIII glycoforms with triglyceride levels was found to be largely independent of body mass index. Due to its precision and throughput, the new workflow will allow studying the role of apo-CIII in the regulation of lipid metabolism in various disease settings.

Serum N-glycan profiles differ for various breast cancer subtypes.

Breast cancer is the most prevalent cancer in women. Early detection of this disease improves survival and therefore population screenings, based on mammography, are performed. However, the sensitivity of this screening modality is not optimal and new screening methods, such as blood tests, are being explored. Most of the analyses that aim for early detection focus on proteins in the bloodstream. In this study, the biomarker potential of total serum N-glycosylation analysis was explored with regard to detection of breast cancer. In an age-matched case-control setup serum protein N-glycan profiles from 145 breast cancer patients were compared to those from 171 healthy individuals. N-glycans were enzymatically released, chemically derivatized to preserve linkage-specificity of sialic acids and characterized by high resolution mass spectrometry. Logistic regression analysis was used to evaluate associations of specific N-glycan structures as well as N-glycosylation traits with breast cancer. In a case-control comparison three associations were found, namely a lower level of a two triantennary glycans and a higher level of one tetraantennary glycan in cancer patients. Of note, various other N-glycomic signatures that had previously been reported were not replicated in the current cohort. It was further evaluated whether the lack of replication of breast cancer N-glycomic signatures could be partly explained by the heterogenous character of the disease since the studies performed so far were based on cohorts that included diverging subtypes in different numbers. It was found that serum N-glycan profiles differed for the various cancer subtypes that were analyzed in this study.

Serum N-Glycome analysis reveals pancreatic cancer disease signatures.

BACKGROUND &AIMS: Pancreatic ductal adenocarcinoma (PDAC) is an aggressive cancer type with loco-regional spread that makes the tumor surgically unresectable. Novel diagnostic tools are needed to improve detection of PDAC and increase patient survival. In this study we explore serum protein N-glycan profiles from PDAC patients with regard to their applicability to serve as a disease biomarker panel. METHODS: Total serum N-glycome analysis was applied to a discovery set (86 PDAC cases/84 controls) followed by independent validation (26 cases/26 controls) using in-house collected serum specimens. Protein N-glycan profiles were obtained using ultrahigh resolution mass spectrometry and included linkage-specific sialic acid information. N-glycans were relatively quantified and case-control classification performance was evaluated based on glycosylation traits such as branching, fucosylation, and sialylation. RESULTS: In PDAC patients a higher level of branching (OR 6.19, P-value 9.21 × 10-11 ) and (antenna)fucosylation (OR 13.27, P-value 2.31 × 10-9 ) of N-glycans was found. Furthermore, the ratio of α2,6- vs α2,3-linked sialylation was higher in patients compared to healthy controls. A classification model built with three glycosylation traits was used for discovery (AUC 0.88) and independent validation (AUC 0.81), with sensitivity and specificity values of 0.85 and 0.71 for the discovery set and 0.75 and 0.72 for the validation set. CONCLUSION: Serum N-glycome analysis revealed glycosylation differences that allow classification of PDAC patients from healthy controls. It was demonstrated that glycosylation traits rather than single N-glycan structures obtained in this clinical glycomics study can serve as a basis for further development of a blood-based diagnostic test.

Dried blood spot N-glycome analysis by MALDI mass spectrometry.

Body fluid N-glycome analysis as well as glyco-proteoform profiling of existing protein biomarkers potentially provides a stratification layer additional to quantitative, diagnostic protein levels. For clinical omics applications, the collection of a dried blood spot (DBS) is increasingly pursued as an alternative to sampling milliliters of peripheral blood. Here we evaluate DBS cards as a blood collection strategy for protein N-glycosylation analysis aiming for high-throughput clinical applications. A protocol for facile N-glycosylation profiling from DBS is developed that includes sialic acid linkage differentiation. This protocol is based on a previously established total plasma N-glycome mass spectrometry (MS) method, with adjustments for the analysis of DBS specimens. After DBS-punching and protein solubilization N-glycans are released, followed by chemical derivatization of sialic acids and MS-measurement of N-glycan profiles. With this method, more than 80 different glycan structures are identified from a DBS, with RSDs below 10% for the ten most abundant glycans. N-glycan profiles of finger-tip blood and venous blood are compared and short-term stability of DBS is demonstrated. This method for fast N-glycosylation profiling of DBS provides a minimally invasive alternative to conventional serum and plasma protein N-glycosylation workflows. With simplified blood sampling this DBS approach has vast potential for clinical glycomics applications.

Automated Plasma Glycomics with Linkage-Specific Sialic Acid Esterification and Ultrahigh Resolution MS.

High-throughput mass spectrometry (MS) glycomics is an emerging field driven by technological advancements including sample preparation and data processing. Previously, we reported an automated protocol for the analysis of N-glycans released from plasma proteins that included sialic acid derivatization with linkage-specificity, namely, ethylation of α2,6-linked sialic acid residues and lactone formation of α2,3-linked sialic acids. In the current study, each step in this protocol was further optimized. Method improvements included minimizing the extent of side-reaction during derivatization, an adjusted glycan purification strategy and mass analysis of the released N-glycans by ultrahigh resolution matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance MS. The latter resolved peak overlap and simplified spectral alignment due to high mass measurement precision. Moreover, this resulted in more confident glycan assignments and improved signal-to-noise for low-abundant species. The performance of the protocol renders high-throughput applications feasible in the exciting field of clinical glycomics.

Plasma N-Glycan Signatures Are Associated With Features of Inflammatory Bowel Diseases.

BACKGROUND & AIMS: Biomarkers are needed for early detection of Crohn's disease (CD) and ulcerative colitis (UC) or to predict patient outcomes. Glycosylation is a common and complex posttranslational modification of proteins that affects their structure and activity. We compared plasma N-glycosylation profiles between patients with CD or UC and healthy individuals (controls). METHODS: We analyzed the total plasma N-glycomes of 2635 patients with inflammatory bowel diseases and 996 controls by mass spectrometry with a linkage-specific sialic acid derivatization technique. Plasma samples were acquired from 2 hospitals in Italy (discovery cohort, 1989 patients with inflammatory bowel disease [IBD] and 570 controls) and 1 medical center in the United States (validation cohort, 646 cases of IBD and 426 controls). Sixty-three glycoforms met our criteria for relative quantification and were extracted from the raw data with the software MassyTools. Common features shared by the glycan compositions were combined in 78 derived traits, including the number of antennae of complex-type glycans and levels of fucosylation, bisection, galactosylation, and sialylation. Associations of plasma N-glycomes with age, sex, CD, UC, and IBD-related parameters such as disease location, surgery and medication, level of C-reactive protein, and sedimentation rate were tested by linear and logistic regression. RESULTS: Plasma samples from patients with IBD had a higher abundance of large-size glycans compared with controls, a decreased relative abundance of hybrid and high-mannose structures, lower fucosylation, lower galactosylation, and higher sialylation (α2,3- and α2,6-linked). We could discriminate plasma from patients with CD from that of patients with UC based on higher bisection, lower galactosylation, and higher sialylation (α2,3-linked). Glycosylation patterns were associated with disease location and progression, the need for a more potent medication, and surgery. These results were replicated in a large independent cohort. CONCLUSIONS: We performed high-throughput analysis to compare total plasma N-glycomes of individuals with vs without IBD and to identify patterns associated with disease features and the need for treatment. These profiles might be used in diagnosis and for predicting patients' responses to treatment.

Serum Protein N-Glycosylation Changes with Rheumatoid Arthritis Disease Activity during and after Pregnancy.

Symptoms of rheumatoid arthritis (RA) improve during pregnancy, a phenomenon that was found to be associated with N-glycosylation changes of immunoglobulin G. Recent advances in high-throughput glycosylation analysis allow the assessment of the N-glycome of human sera as well. The aim of this study was to identify new protein N-glycosylation properties that associate with changes in RA disease activity during and after pregnancy. A longitudinal cohort of serum samples was collected during 285 pregnancies (32 control individuals and 253 RA patients). Per individual one sample was collected before conception, three during pregnancy, and three after delivery. Released serum protein N-glycans were measured by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS) after employing chemical modification of the sialic acids to allow discrimination of sialic acid linkage isomers. Serum protein N-glycosylation showed strongly modified during pregnancy, with similar changes visible in control individuals and RA pregnancies. Namely, a decrease in bisection and an increase in galactosylation in diantennary glycans were found, as well as an increase in tri- and tetraantennary species and α2,3-linked sialylation thereof. The change in RA disease activity [DAS28(3)-CRP] proved negatively associated with the galactosylation of diantennary N-glycans, and positively with the sialylation of triantennary fucosylated species (A3FGS). While the protein source of the novel finding A3FGS is thus far unknown, its further study may improve our understanding of the etiology of RA disease severity.

Automation of High-Throughput Mass Spectrometry-Based Plasma N-Glycome Analysis with Linkage-Specific Sialic Acid Esterification.

Glycosylation is a post-translational modification of key importance with heterogeneous structural characteristics. Previously, we have developed a robust, high-throughput MALDI-TOF-MS method for the comprehensive profiling of human plasma N-glycans. In this approach, sialic acid residues are derivatized with linkage-specificity, namely the ethylation of α2,6-linked sialic acid residues with parallel lactone formation of α2,3-linked sialic acids. In the current study, this procedure was used as a starting point for the automation of all steps on a liquid-handling robot system. This resulted in a time-efficient and fully standardized procedure with throughput times of 2.5 h for a first set of 96 samples and approximately 1 h extra for each additional sample plate. The mass analysis of the thus-obtained glycans was highly reproducible in terms of relative quantification, with improved interday repeatability as compared to that of manual processing.

Quantification of serum apolipoproteins A-I and B-100 in clinical samples using an automated SISCAPA-MALDI-TOF-MS workflow.

A fully automated workflow was developed and validated for simultaneous quantification of the cardiovascular disease risk markers apolipoproteins A-I (apoA-I) and B-100 (apoB-100) in clinical sera. By coupling of stable-isotope standards and capture by anti-peptide antibodies (SISCAPA) for enrichment of proteotypic peptides from serum digests to matrix-assisted laser desorption/ionization time-of-flight (MALDI-TOF) MS detection, the standardized platform enabled rapid, liquid chromatography-free quantification at a relatively high throughput of 96 samples in 12h. The average imprecision in normo- and triglyceridemic serum pools was 3.8% for apoA-I and 4.2% for apoB-100 (4 replicates over 5 days). If stored properly, the MALDI target containing enriched apoA-1 and apoB-100 peptides could be re-analyzed without any effect on bias or imprecision for at least 7 days after initial analysis. Validation of the workflow revealed excellent linearity for daily calibration with external, serum-based calibrators (R(2) of 0.984 for apoA-I and 0.976 for apoB-100 as average over five days), and absence of matrix effects or interference from triglycerides, protein content, hemolysates, or bilirubins. Quantification of apoA-I in 93 normo- and hypertriglyceridemic clinical sera showed good agreement with immunoturbidimetric analysis (slope = 1.01, R(2) = 0.95, mean bias = 4.0%). Measurement of apoB-100 in the same clinical sera using both methods, however, revealed several outliers in SISCAPA-MALDI-TOF-MS measurements, possibly as a result of the lower MALDI-TOF-MS signal intensity (slope = 1.09, R(2) = 0.91, mean bias = 2.0%). The combination of analytical performance, rapid cycle time and automation potential validate the SISCAPA-MALDI-TOF-MS platform as a valuable approach for standardized and high-throughput quantification of apoA-I and apoB-100 in large sample cohorts.

Solid-phase extraction strategies to surmount body fluid sample complexity in high-throughput mass spectrometry-based proteomics.

For large-scale and standardized applications in mass spectrometry- (MS-) based proteomics automation of each step is essential. Here we present high-throughput sample preparation solutions for balancing the speed of current MS-acquisitions and the time needed for analytical workup of body fluids. The discussed workflows reduce body fluid sample complexity and apply for both bottom-up proteomics experiments and top-down protein characterization approaches. Various sample preparation methods that involve solid-phase extraction (SPE) including affinity enrichment strategies have been automated. Obtained peptide and protein fractions can be mass analyzed by direct infusion into an electrospray ionization (ESI) source or by means of matrix-assisted laser desorption ionization (MALDI) without further need of time-consuming liquid chromatography (LC) separations.

Standardized and automated solid-phase extraction procedures for high-throughput proteomics of body fluids.

In order to balance the speed of analytical sample preparation procedures with mass spectrometry (MS)-based clinical proteomics the application of high-throughput robotic systems for body fluid workup is essential. In this paper we describe the implementation of various solid-phase extraction (SPE) sample preparation protocols on two different platforms, namely: 1) Magnetic bead-based SPE of peptides and proteins from body fluids on a Hamilton liquid handling workstation; 2) Cartridge-based SPE on a SPARK Symbiosis system. All SPE protocols were optimized for MS-based proteomics and compared with respect to obtained peptide- and protein profiles. Throughput numbers that were achieved in a 24 hour time frame for the sample workup procedures were more than 700 samples for the magnetic bead-based method and over 1000 samples for the cartridge-based method.

Identification of human serum peptides in fourier transform ion cyclotron resonance precision profiles.

The continuous efforts to find new prognostic or diagnostic biomarkers have stimulated the use of mass spectrometry (MS) profiles in a clinical setting. In the early days (about one decade ago), a single low-resolution mass spectrum derived from an individual's body fluid was used for comparative studies. However, a peptide profile of a complex mixture is most informative when recorded on an ultrahigh resolution instrument such as a Fourier transform ion cyclotron resonance (FTICR) mass spectrometer. In this study we show the benefits of the ultrahigh resolving power and the high mass accuracy and precision provided by an FTICR mass spectrometer equipped with a 15-tesla magnet. The ultrahigh-resolution data not only allow assignment of fragment ions with high charge states (4+, 5+) but also enhance confidence of human serum peptide identifications from tandem MS experiments. This is exemplified with collision-induced dissociation (CID) and electron transfer dissociation (ETD) data of middle-down-sized endogenous or protein-breakdown peptides that are of interest in biomarker discovery studies.

Metabonomic investigation of human Schistosoma mansoni infection.

Schistosomiasis is a parasitic infection that is endemic in many developing countries in the tropics and subtropics afflicting more than 207 million people primarily in rural areas. After malaria, it is the second most important parasitic infection in terms of socio-economic and public health. Investigation of the host-parasite interaction at the molecular level and identification of biomarkers of infection and infection-related morbidity would be of value for improved strategies for treatment and morbidity control. To this end, we conducted a nuclear magnetic resonance (NMR) based metabonomics study involving a well-characterized cohort of 447 individuals from a rural area in Uganda near Lake Victoria with a high prevalence of Schistosoma mansoni, a species predominantly occurring in Africa including Madagascar and parts of South America. Cohort samples were collected from individuals at five time-points, before and after (one or two times) chemotherapy with praziquantel (PZQ). Using supervised multivariate statistical analysis of the recorded one-dimensional (1D) NMR spectra, we were able to discriminate infected from uninfected individuals in two age groups (children and adults) based on differences in their urinary profiles. The potential molecular markers of S. mansoni infection were found to be primarily linked to changes in gut microflora, energy metabolism and liver function. These findings are in agreement with data from earlier studies on S. mansoni infection in experimental animals and thus provide corroborating evidence for the existence of metabolic response specific for this infection.

Automated imaging MS: Toward high throughput imaging mass spectrometry.

The term molecular histology has been used to convey the potential of imaging mass spectrometry to describe tissue by its constituent peptides and proteins, and to link this with established histological features. The low throughput of imaging mass spectrometry has been one of the factors inhibiting a full investigation of the clinical potential of molecular histology. Here we report the development of an automated set-up, consisting of a controlled environment sample storage chamber, a sample loading robot, and a MALDI-TOF/TOF mass spectrometer, all controlled by a single user interface. The automated set-up is demonstrated to have the positional stability and experimental reproducibility necessary for its clinical application.

Case-control breast cancer study of MALDI-TOF proteomic mass spectrometry data on serum samples.

We introduce mass spectrometry proteomic research for diagnosis from a clinical perspective, with special reference to early-stage breast cancer detection. The nature of SELDI and MALDI mass spectrometric measurement is discussed. We explain how the mass spectral data arising from this technology may be viewed as a new data type. Some of the properties of the data are discussed and we show how such spectra may be interpreted. Sample preprocessing for mass spectrometry is introduced and a literature review of research in clinical proteomics is presented. Finally, we provide a detailed description of the study design on the breast cancer case-control study which is investigated in this special issue.

Detection of colorectal cancer using MALDI-TOF serum protein profiling.

Serum protein profiling is a promising approach for classification of cancer versus non-cancer samples. The objective of our study was to assess the feasibility of mass spectrometry based protein profiling for the discrimination of colorectal cancer (CRC) patients from healthy individuals. In a randomized block design, pre-operative serum samples obtained from 66 colorectal cancer patients and 50 controls were used to generate MALDI-TOF protein profiles. After pre-processing of the spectra, linear discriminant analysis with double cross-validation was used to classify protein profiles. A total recognition rate (92.6%), sensitivity (95.2%) and specificity (90.0%) for the detection of CRC were shown. The area under the curve of the classifier was 97.3%, and demonstrated the high, significant separation power of the classifier. Double cross-validation shows that classification can be attributed to information in the protein profile. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of colorectal cancer.

Reliability of human serum protein profiles generated with C8 magnetic beads assisted MALDI-TOF mass spectrometry.

Protein profiling with mass spectrometry is a promising approach for classification and identification of biomarkers; however, there is debate about measurement quality and reliability. Here, we present a pipeline for preprocessing, statistical data analysis and presentation. Serum samples of 16 healthy individuals are used to generate protein profiles with high-resolution MALDI-TOF after isolation of peptides with C8 magnetic beads. Analysis of variance was performed after binning, baseline correction and normalization of the mean spectra. Relative variations in the spectra are expressed as coefficient of variation, which depending on the respective preanalytical variation parameter investigated, was found to range between 0.15 and 0.67 in this study. With this novel method, the reproducibility of our protein profiling procedure could be quantified. We showed that circadian rhythm and the number of freeze-thaw cycles had relatively limited influence on serum protein profiles, whereas the period between collection and serum centrifugation had a more pronounced effect.