Multiple reaction monitoring or MRM is widely acknowledged for its accuracy of quantitation. The applications have mostly been in the analysis of small molecules and proteins, but its utility is expanding. Protein glycosylation was recently identified as a new paradigm in biomarker discovery for health and disease. A number of recent studies have now identified differential glycosylation patterns associated with health and disease states, including aging, pregnancy, rheumatoid arthritis and different types of cancer. While the use of MRM in clinical glycomics is still in its infancy, it can likely play a role in the quantitation of protein glycosylation in the clinical setting. Here, we aim to review the current advances in the nascent application of MRM in the field of glycomics.

Studies aimed toward glycan biomarker discovery have focused on glycan characterization by the global profiling of released glycans. Site-specific glycosylation analysis is less developed but may provide new types of biomarkers with higher sensitivity and specificity. Quantitation of peptide-conjugated glycans directly facilitates the differential analysis of distinct glycoforms associated with specific proteins at distinct sites. We have developed a method using MRM to monitor protein glycosylation normalized to absolute protein concentrations to examine quantitative changes in glycosylation at a site-specific level. This new approach provides information regarding both the absolute amount of protein and the site-specific glycosylation profile and will thus be useful to determine if altered glycosylation profiles in serum/plasma are due to a change in protein glycosylation or a change in protein concentration. The remarkable sensitivity and selectivity of MRM enable the detection of low abundance IgG glycopeptides, even when IgG was digested directly in serum with no cleanup prior to the liquid chromatography. Our results show a low limit of detection of 60 amol and a wide dynamic range of 3 orders magnitude for IgG protein quantitation. The results show that IgG glycopeptides can be analyzed directly from serum (without enrichment) and yield more accurate abundances when normalized to the protein content. This report represents the most comprehensive study so far of the use of multiple reaction monitoring for the quantitation of glycoproteins and their glycosylation patterns in biofluids.

Human milk oligosaccharides (HMO) are one of the major components of human milk. HMO are non-digestible by the human gut, where they are known to play important functions as prebiotics and decoys for binding pathogens. Moreover, it has been proposed that HMO may provide sialic acids to the infant that are important in brain development, however this would require absorption of HMO into the bloodstream. HMO have consistently been found in the urine of humans and other mammals, suggesting systemic absorption. Here, we present a procedure for the profiling of milk oligosaccharides (MO) in plasma samples obtained from 13 term infants hospitalized for surgery for congenital heart disease. The method comprises protein denaturation, oligosaccharide reduction, and porous graphitized carbon solid phase extraction for purification followed by analysis using nHPLC-PGC-chip-TOF-MS. Approximately 15 free MO were typically observed in the plasma of human infants, including LNT, LDFP, LNFT, 3'SL, 6'SL, 3'SLN, and 6'SLN, of which the presence was confirmed using fragmentation studies. A novel third isomer of SLN, not found in human or bovine milk was also consistently detected. Differences in the free MO profiles were observed between infants that were totally formula-fed and infants that received at least some part breast milk. Our results indicate that free MO similar in structure to those found in human milk and urine are present in the blood of infants. The method and results presented here will facilitate further research toward the possible roles of free MO in the development of the infant.

Glycomic and glycoproteomic analyses involve the characterization of oligosaccharides (glycans) conjugated to proteins. Glycans are produced through a complicated nontemplate driven process involving the competition of enzymes that extend the nascent chain. The large diversity of structures, the variations in polarity of the individual saccharide residues, and the poor ionization efficiencies of glycans all conspire to make the analysis arguably much more difficult than any other biopolymer. Furthermore, the large number of glycoforms associated with a specific protein site makes it more difficult to characterize than any post-translational modification. Nonetheless, there have been significant progress, and advanced separation and mass spectrometry methods have been at its center and the main reason for the progress. While glycomic and glycoproteomic analyses are still typically available only through highly specialized laboratories, new software and workflow is making it more accessible. This review focuses on the role of mass spectrometry and separation methods in advancing glycomic and glycoproteomic analyses. It describes the current state of the field and progress toward making it more available to the larger scientific community.

A comprehensive glycan map was constructed for the top eight abundant glycoproteins in plasma using both specific and nonspecific enzyme digestions followed by nano liquid chromatography (LC)-chip/quadrupole time-of-flight mass spectrometry (MS) analysis. Glycopeptides were identified using an in-house software tool, GPFinder. A sensitive and reproducible multiple reaction monitoring (MRM) technique on a triple quadrupole MS was developed and applied to quantify immunoglobulins G, A, M, and their site-specific glycans simultaneously and directly from human serum/plasma without protein enrichments. A total of 64 glycopeptides and 15 peptides were monitored for IgG, IgA, and IgM in a 20 min ultra high performance (UP)LC gradient. The absolute protein contents were quantified using peptide calibration curves. The glycopeptide ion abundances were normalized to the respective protein abundances to separate protein glycosylation from protein expression. This technique yields higher method reproducibility and less sample loss when compared with the quantitation method that involves protein enrichments. The absolute protein quantitation has a wide linear range (3-4 orders of magnitude) and low limit of quantitation (femtomole level). This rapid and robust quantitation technique, which provides quantitative information for both proteins and glycosylation, will further facilitate disease biomarker discoveries.

With expanded access to, and decreased costs of, mass spectrometry, investigators are collecting and analyzing multiple biological matrices from the same subject such as serum, plasma, tissue and urine to enhance biomarker discoveries, understanding of disease processes and identification of therapeutic targets. Commonly, each biological matrix is analyzed separately, but multivariate methods such as MANOVAs that combine information from multiple biological matrices are potentially more powerful. However, mass spectrometric data typically contain large amounts of missing values, and imputation is often used to create complete data sets for analysis. The effects of imputation on multiple biological matrix analyses have not been studied. We investigated the effects of seven imputation methods (half minimum substitution, mean substitution, k-nearest neighbors, local least squares regression, Bayesian principal components analysis, singular value decomposition and random forest), on the within-subject correlation of compounds between biological matrices and its consequences on MANOVA results. Through analysis of three real omics data sets and simulation studies, we found the amount of missing data and imputation method to substantially change the between-matrix correlation structure. The magnitude of the correlations was generally reduced in imputed data sets, and this effect increased with the amount of missing data. Significant results from MANOVA testing also were substantially affected. In particular, the number of false positives increased with the level of missing data for all imputation methods. No one imputation method was universally the best, but the simple substitution methods (Half Minimum and Mean) consistently performed poorly.

Prebiotics are nondigestible substrates that stimulate the growth of beneficial microbes in the human intestine. Galactooligosaccharides (GOS) are food ingredients that possess prebiotic properties, in particular, promoting the growth of bifidobacteria in situ. However, precise mechanistic details of GOS consumption by bifidobacteria remain poorly understood. Because GOS are mixtures of polymers of different lengths and linkages, there is interest in determining which specific structures provide prebiotic effects to potentially create better supplements. This paper presents a method comprising porous graphitic carbon separation, isotopic labeling, and mass spectrometry analysis for the structure-specific analysis of GOS isomers and their bacterial consumption rate. Using this strategy, the differential bacterial consumption of GOS by the bifidobacteria species Bifidobacterium longum subsp. infantis, Bifidobacterium animalis subsp. lactis, and Bifidobacterium adolescentis was determined, indicating that the use of specific GOS isomers in infant formula may provide enrichment of distinct species.

Motivation

High through-put mass spectrometry (MS) is now being used to profile small molecular compounds across multiple biological sample types from the same subjects with the goal of leveraging information across biospecimens. Multivariate statistical methods that combine information from all biospecimens could be more powerful than the usual univariate analyses. However, missing values are common in MS data and imputation can impact between-biospecimen correlation and multivariate analysis results.

Results

We propose two multivariate two-part statistics that accommodate missing values and combine data from all biospecimens to identify differentially regulated compounds. Statistical significance is determined using a multivariate permutation null distribution. Relative to univariate tests, the multivariate procedures detected more significant compounds in three biological datasets. In a simulation study, we showed that multi-biospecimen testing procedures were more powerful than single-biospecimen methods when compounds are differentially regulated in multiple biospecimens but univariate methods can be more powerful if compounds are differentially regulated in only one biospecimen.

Availability and implementation

We provide R functions to implement and illustrate our method as supplementary information CONTACT: sltaylor@ucdavis.eduSupplementary information: Supplementary data are available at Bioinformatics online.

An absolute quantitation method for measuring free human milk oligosaccharides (HMOs) in milk samples was developed using multiple reaction monitoring (MRM). To obtain the best sensitivity, the instrument conditions were optimized to reduce the source and postsource fragmentation prior to the quadrupole transmission. Fragmentation spectra of HMOs using collision-induced dissociation were studied to obtain the best characteristic fragments. At least two MRM transitions were used to quantify and identify each structure in the same run. The fragment ions corresponded to the production of singly charged mono-, di-, and trisaccharide fragments. The sensitivity and accuracy of the quantitation using MRM were determined, with the detection limit in the femtomole level and the calibration range spanning over 5 orders of magnitude. Seven commercial HMO standards were used to create calibration curves and were used to determine a universal response for all HMOs. The universal response factor was used to estimate absolute amounts of other structures and the total oligosaccharide content in milk. The quantitation method was applied to 20 human milk samples to determine the variations in HMO concentrations from women classified as secretors and nonsecretors, a phenotype that can be identified by the concentration of 2'-fucosylation in their milk.

Protein glycosylation fingerprints are widely recognized as potential markers for disease states, and indeed differential glycosylation has been identified in multiple types of autoimmune diseases and several types of cancer. However, releasing the glycans leave the glycoproteins unknown; therefore, there exists a need for high-throughput methods that allow quantification of site- and protein-specific glycosylation patterns from complex biological mixtures. In this study, a targeted multiple reaction monitoring (MRM)-based method for the protein- and site-specific quantitation involving serum proteins immunoglobulins A, G and M, alpha-1-antitrypsin, transferrin, alpha-2-macroglobulin, haptoglobin, alpha-1-acid glycoprotein and complement C3 was developed. The method is based on tryptic digestion of serum glycoproteins, followed by immediate reverse phase UPLC-QQQ-MS analysis of glycopeptides. To quantitate protein glycosylation independent of the protein serum concentration, a nonglycosylated peptide was also monitored. Using this strategy, 178 glycopeptides and 18 peptides from serum glycoproteins are analyzed with good repeatability (interday CVs of 3.65-21-92%) in a single 17 min run. To assess the potential of the method, protein glycosylation was analyzed in serum samples from ovarian cancer patients and controls. A training set consisting of 40 cases and 40 controls was analyzed, and differential analyses were performed to identify aberrant glycopeptide levels. All findings were validated in an independent test set (n = 44 cases and n = 44 controls). In addition to the differential glycosylation on the immunoglobulins, which was reported previously, aberrant glycosylation was also observed on each of the glycoproteins, which could be corroborated in the test set. This report shows the development of a method for targeted protein- and site-specific glycosylation analysis and the potential of such methods in biomarker development.