368

DOI 10.1002/prca.201400184

Proteomics Clin. Appl. 2015, 9, 368–382

REVIEW

Relative versus absolute quantitation in disease glycomics Edward S.X. Moh, Morten Thaysen-Andersen and Nicolle H. Packer Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia The glycome of a diagnostic biological material such as blood, urine, saliva, tissue, or cell cultures comprises of a vast array of structurally distinct glycans attached to the protein complement. Aberrant glycan structures and distributions result from changes in specific glycosyltransferase activities and have different biological significance, making proper quantitation of glycans highly important. In this review, common HPLC/CE and LC-MS/MS-based methods for glycomics, their advantages and disadvantages, will be discussed with respect to the main quantitative strategies. With the increasing interest in absolute quantitation for glycomics, we discuss absolute and relative glycome quantitation and how it affects the resulting conclusions drawn from glycomics studies. We argue that while absolute quantitation of glycomes may be attractive for some areas of clinical glycomics, relative quantitation of glycans remains the most informative and time/cost-effective method to obtain biological insight into the regulation of the cellular glycosylation machinery and the synthesis of the resultant glycan structures in most research questions due to the enzymatic relatedness of the biosynthesized glycans. Recent developments in multiplexing of glycomes by the introduction of stable isotopic labeling of glycans show promise for providing another level of information to the existing benefits of relative quantitation.

Received: November 14, 2014 Revised: December 21, 2014 Accepted: February 10, 2015

Keywords: Absolute quantitation / Glycans / Glycomics / Glycosylation / Relative quantitation

1

Introduction

Glycosylation is a common PTM of proteins involving the conjugation of carbohydrates/oligosaccharides (hereafter called glycans) to polypeptides [1]. In humans, glycans are most commonly attached to asparagine residues (termed N-linked glycosylation) localized in asparaginexxx-threonine/serine (xxx ࣔ proline) consensus sequences or to motif-free threonine or serine residues (O-linked (mucin type) glycosylation). N-linked glycans have a common pentasaccharide core structure (mannose(Man)3 Nacetylglucosamine(GlcNAc)2 ) that can be heterogeneously elongated into a broad spectrum of mature N-glycans [2]. O-linked glycans, in contrast, which are generally Correspondence: Professor Nicolle H. Packer, Department of Chemistry and Biomolecular Sciences, Macquarie University, NSW-2109 North Ryde, Australia Email: [email protected] Fax: +61-2-9850-8313 Abbreviations: EIC, extracted ion chromatograms; GlcNAc, N-acetylglucosamine; HILIC, hydrophilic interaction liquid chromatography; PGC, porous graphitized carbon; PNGase, peptide-N-glycosidase  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

shorter glycans, display eight core structures from the N-acetylgalactosamine base monosaccharide [3]. Although other types of glycosylation exist, this review will focus on the N- and O-linked protein glycosylation. Glycosylation is a nontemplate driven process facilitated by multiple competing glycosidases and glycosyltransferases in the endoplasmic reticulum and the Golgi apparatus as glycoproteins traffic and mature through the secretory pathway [2]. Unlike DNA and proteins, glycans are often branched biopolymers comprised of monosaccharides connected by a variety of linkage forms; however, naturally occurring glycans are restricted by the processing enzymes to form only a subset of the possible branch and linkage permutations. The incomplete nature of the enzymatic biosynthetic reactions often results in micro- (glycan structure) and macro- (site occupation) heterogeneity yielding populations of structurally related protein glycoforms. Glycans are biologically important biomolecules displaying independent or modulatory functions on their carrier proteins including cell–cell communication, recognition, and adhesive abilities and may modulate the protein solubility and stability [1, 3, 4]. Genetic defects causing dysregulation of the Colour Online: See the article online to view Figs. 1–3 in colour. www.clinical.proteomics-journal.com

Proteomics Clin. Appl. 2015, 9, 368–382

glycosylation processes may lead to congenital disorders of glycosylation [5]. In addition, altered glycosylation may be a cause and/or result of multiple other pathological conditions including, but not limited to, cystic fibrosis [6], multiple types of cancers such as breast, ovarian, colon, and prostate cancer [7–16], inflammation [17] and infection [18,19], and immunity [20, 21]. Hence, mapping the protein glycome and its regulation in normal and disease states will contribute to advancing our understanding of disease mechanisms. In addition, aberrant glycans may serve the purpose of complementing other types of biomarkers for the early detection and recurrence of disease [22]. Accurate and detailed structural characterization, including quantitation, of glycan populations released from the total protein component of biological samples is important, since the dense carbohydrate layer (glycocalyx) on the surface of all cells presents a complex landscape of glycan structures that interact directly with the extracellular matrix [23], other cells [24, 25], the immune system [20, 26] and invading microorganisms [18, 27], to name a few. In addition, the type and quantity of the individual glycans within a given glycoprofile provide mechanistic insights into the activity and regulation of the well-described mammalian glycosylation pathway [25, 28, 29], both on the resultant glycosylation of the total protein complement produced by the particular cell(s) as well as on specific proteins. This becomes particularly true when global glycan profiles of the cellular proteins from different origins and perturbations are compared, and the aberrant glycosylation resulting from dysregulated enzymes identified [7,10,11,13,14,16,22,25,29–35]. The glycome thus offers potential biomarkers of disease, an insight into the mechanisms of interaction between cells and a window into the physiological status of the glycosylation machinery. Inhibition or stimulation of glycan modulating enzymes in the glycosylation pathway, in particular the rate limiting ones, can directly result in noticeable changes in the whole cell protein glycosylation, which may be detected upon accurate quantitative profiling of the total glycome [16]. Thus, glycome mapping can provide valuable biological information even when analyzed independently of the protein carrier, since all proteins in a cell are glycosylated by the same pathway. Qualitative differences in single glycan structures (absence/presence) are rarely observed in glycobiology when disease glycomes are being interrogated relative to their healthy counterpart; regulation is instead often detected in the quantitative shift in the relative abundances of the glycans (glycoforms) present in the samples being compared. Accurate relative quantitation is thus absolutely critical in analytical glycoscience and significant efforts have been undertaken to develop such accurate methods [36]. Glycobiological function(s) often can be learnt from less-than-complete structural information of glycoproteins, e.g. at the monosaccharide compositional level [37]; however, in other cases, the functional understanding of glycobiology is hidden in the fine details of the glycan substructures presented to the cellular surrounds. Glycoform separation and detection is thus highly  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

369 important since slight variations in particular of the terminal glycan determinants can define the cell–cell and cell– protein interactions [23]. For example, isobaric glycans isomers with the same monosaccharide composition and mass can differ in their biological activity, e.g. a monosialylated, monoantennary, bisecting core fucosylated glycan is known to convey different chemical information than a triantennary glycan with a sialyl-Lewisx determinant (Fig. 1). In addition, the ␤1,4-GlcNAc residues in the bisecting N-linked glycans have been shown to affect protein–protein interactions [28], while the ␤1,6-GlcNAc branched N-linked isomer is upregulated in cancer [14, 24, 25]. Core fucosylation of N-glycans on the other hand is a proposed marker for hepatocarcinoma [28, 38], and is also involved in protein–protein interactions, whereas the outer-arm fucose forming the sialyl-Lewisx determinant, which is found on the zona pellucida of the human egg at high abundance, affects the sperm-egg binding during fertilization [23, 39]. Protein glycosylation remains an analytical challenge to characterize due to the extensive micro- and macroheterogeneity formed by multiple layers of diversity at the primary glycan structural level. Variations in the monosaccharide compositions, overall topology/branching patterns, and linkage types are common glycan features, which create a spectrum of closely related glycan species. Thus, the amount of information needed to characterize a glycoprotein is significantly larger than for unmodified proteins and proteins displaying single moiety modifications such as phosphorylation and acetylation. Separation techniques based on HPLC and CE conjugated to MS have been established for the detailed analysis of the structure of purified glycoproteins at the intact glycan or glycopeptide level [12, 40–47]. There is however a growing need for analyzing complex glycoprotein mixtures, reflecting the total biomolecular complement of a biological sample. Significant efforts are providing methodologies of relatively high throughput and large-scale analysis within the field of glycoscience. Such system-wide technologies designed to characterize different structural aspects of glycosylated proteins may crudely be divided into three types of -omics disciplines, i.e. proteomics, glycoproteomics, and glycomics (Fig. 2). As briefly described below, these levels yield complementary information on the glycoprotein structures at the system-wide level and can thus be used in combination to assess different aspects of their regulation in cells, tissues, and body fluids. Proteomics-based approaches for N-glycosylation analysis commonly use deglycosylated peptide analytes to determine glycoprotein identity and glycosylation sites in a systemwide manner [48–52]. This involves using column/beadsbased methods such as hydrazine conjugation [50, 53] and hydrophilic interaction liquid chromatography (HILIC), titanium dioxide [43, 54, 55] and lectin-based enrichment [48] either pre- (glycoprotein enrichment) or post- (glycopeptide enrichment) protease digestion and N-glycosidase F (PNGase F)-based glycan release. Such proteomics-style approaches, which typically deal with the qualitative and quantitative www.clinical.proteomics-journal.com

370

E. S. X. Moh et al.

Proteomics Clin. Appl. 2015, 9, 368–382

Figure 1. Schematic representation of two isobaric N-glycan isomers with the same mass and monosaccharide composition (NeuAc1 Gal1 Man3 HexNAc5 Fuc1 ), but have different structural (blue circles) and functional features. The synthesis of these glycofeatures involves the action of multiple glycosyltransferases, and the resulting glycan structure contributes different functionality. Analytical methods such as PGC-LC-MS/MS and UV-HPLC in combination with exoglycosidases treatments are able to differentiate such N-glycan isomers.

analyses of formerly N-glycosylated proteins/peptides by MS detection of the mass change occurring following enzymatic de-N-glycosylation of the previously occupied asparagine residue with or without heavy 18 O-labeled water [48, 50, 56], ignore the glycan structure itself. However, proteome-wide glycoprotein identification and glycosylation site assignment provide useful information to glycobiologists provided correct experimental design is applied to identifying false positive mass changes arising from spontaneous deamidation of the asparagine [36]. Recent reviews have covered the quantitative aspect of glyco-focused (deglycosylated) proteomics [57, 58] and will not be discussed here. Glycoproteomics-based approaches utilize intact glycopeptides to provide direct evidence of the connectivity and heterogeneity of the glycan moieties on specific sites of the polypeptide chain at the system-wide level. Outcomes of glycoproteomics include information about the glycoprotein identity, glycosylation site identity, and its occupancy and information on the attached glycan structure, though monosaccharide composition, rather than detailed glycan structure, is still the most commonly derived information. Such methodologies are still maturing due to the significant analytical challenges associated with the heterogeneous and information-rich glycopeptides in complex peptide mixtures [59]. Tangible examples of powerful glycoproteomic methods detecting thousands of intact glycopeptides from biological samples have recently been published [60, 61]. Label-free quantitative glycoproteomics of native intact glycopeptides often relies on the assumption that glycopeptide populations of the same family (same peptide, different glycan) display  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

similar LC retention and MS ionization properties, which has been shown to be a reasonably accurate assumption, particularly for N-glycopeptides (and N-glycans) when the analytical parameters are carefully considered and optimized and when glycopeptides of the same net charge are being relatively quantified [61–63]. We have recently discussed the quantitative aspects of glycoproteomics in the context of analytical glycoscience in a separate review [59], and this aspect will not be covered further here. Glycomics-based approaches analyze entire glycan populations released from the biological material (i.e. protein and/or lipid mixtures) and aim to identify and accurately quantitate the individual glycan structural species present in the global population. Unlike proteomics and glycoproteomics where unique peptides can be used to identify and quantitate the carrier proteins [64], glycomics differs by the fact that the individual glycan species are pooled from the multiple proteins upon release of the protein mixture being investigated. This protein carrier-unspecific approach reflects the comparatively finite number of glycan species synthesized by the cell. Hence, unlike the other related omics approaches, the analytical challenges in glycomics are not the large number of analytes, but their close structural relatedness, e.g. identical residue and oligosaccharide masses and similar physicochemical properties. Several semiautomated HPLC/CE interfaced with MS-based glycomics workflows are available for low/medium through-put N- and O-glycome profiling [12, 42, 47, 60, 65–69]. Enabled by labeling strategies, chromatographic phases with unique properties and sensitive mass spectrometers, low amounts of glycans (to femtomolar www.clinical.proteomics-journal.com

Proteomics Clin. Appl. 2015, 9, 368–382

371

Figure 2. Schematic illustration showing the three levels of “glyco-analytes” (i.e. deglycosylated peptides, glycopeptides, glycans) and how they can be used to generate different information of the glycoproteins (bold boxes, bottom) from complex biological specimens, e.g. body fluids, tissue, and cells using (glyco-focused) proteomics, glycoproteomics and glycomics.

levels) can effectively be characterized. Once the structures are characterized, the amounts of each glycan species can be quantified to give more information on the glycan determinant landscape presented in the biological sample. Although glycomics intrinsically is limited by ignoring the carrier protein (aglycone) identity, its strength is in generating the highest possible detail of the intact glycan structures that are displayed on the glycoproteins. From a big picture point of view, glycomics resolves the ambiguity of glycosylation compositional alterations usually observed by proteomics methods and more accurately reflects the state of the glycosylation machinery of the cell and the resultant structural glycan products. For example, in cancer, aberrant glycosylation has been widely reported [7,9,10,13–17,22,25,32,34,70], and has often been correlated to an increased abundance of certain glycan features such as increased core fucosylation, sialylation, bisecting GlcNAc, and/or linkage differences in the branching and terminal sialylation regardless of the proteins to which they are attached. These features are often the products of distinct glycosyltransferase(s) and have recently been shown to impact cellular signaling pathways and cancer metastasis [31,71]. In infection-related diseases, adhesion of the microbe or virus, which is the primary step for infection, is often directly related to specific glycan receptors [72]. Regardless of the protein carrying the glycan, microbes, and viruses, such as Pseudomonas aeruginosa [18] and influenza [27, 73], bind to  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

glycan epitopes expressed on the extracellular matrix or cell surface through the use of glycan binding proteins (lectins and adhesins). The degree of sialylation of proteins is another important aspect that could not be accurately characterized from using only proteomics-based methods [74, 75]. Pharmacokinetic properties of therapeutic proteins is highly affected by the overall degree of sialylation of the glycans [4, 76] and long chain polysialic acids conjugated to glycoproteins in the central nervous system have been shown to affect synaptic plasticity [77]. In this review we define absolute quantitation of glycans as the comparison of the detector response of the glycan analyte(s) of interest with the response of a known amount of either similar/identical standards measured externally (with some form of preanalysis calibration curve) or internally (insample spiking). In contrast, relative quantitation will here be used to refer to profiles where an individual glycan is expressed as a percentage of the total glycome, or as a fraction normalized to the most abundant glycan structure within a sample. Often glycoprofiles established by relative quantitation are compared between two or more biological samples to identify the glycans that are differentially expressed in the samples. Utilizing reducing-end labelled glycans carrying recently developed stable isotopes such comparative glycoprofile comparison can be performed within a single analysis using LC [78] or MS [79]. In this review, we will concentrate

www.clinical.proteomics-journal.com

372

E. S. X. Moh et al.

on technical aspects of relative and absolute quantitation in glycomics.

2

Quantitative methods in glycomics

Analytical methods for glycomics generally follow a multistep workflow typically consisting of isolation of protein mixtures from biological specimen, glycan release from the protein carrier(s), glycan derivatization (optional) and/or separation, glycan structure determination and quantitation using UV/fluorescence HPLC/CE or (LC-)MS/MS. Multiple variations and permutations of this generic workflow, in particular at the level of sample preparation and glycan detection, such as LC-UV/fluorescence [45, 66, 80], LC-MS [6, 15, 65], CE-UV/fluorescence [12], CE-MS [81], and direct MS [68, 82, 83] have been presented; the methods of choice are based on laboratory-specific preferences and the specific research question being targeted. The common use of absolute and relative quantitation in the context of the multiple experimental approaches for glycoprofiling is outlined in Fig. 3. Herein, we will focus on quantitative glycomics including quantitative issues relating to the sample preparation prior to glycan detection.

2.1 Glycan release The N- and O-linked glycan release from the protein carriers can be performed by enzymatic and chemical methods from crude protein mixtures in solutions, in gels or bound to solid phases. Lectin-based enrichment or protein-specific affinity purification can isolate specific glycoproteome subsets, although these approaches may suffer from incomplete fractionation or loss of the glycoproteins/glycoforms of interest. For N-linked glycans, enzymatic glycan release by PNGase F, or alternatively PNGase A for N-glycoproteins of plants and other nonmammalian origin, is the most commonly employed method for de-N-glycosylation as the full set of N-glycan species can be released completely from their asparagine anchors in the peptide backbone [84]. The quantitative and unbiased PNGase F-based release requires favorable enzyme/substrate ratios, incubation times, and digestion conditions, i.e. salt/pH/temperature and denatured and/or cysteine-reduced glycoprotein, which tend to be protein specific [85]. Other enzymes such as endoglycosidase H and F3 for high mannose and biantennary complex N-glycoproteins, respectively, may be used for release of specific glycoformsubsets. Endoglycosidase cleavage with these enzymes occurs between the two innermost GlcNAc residues of the N-glycan chitobiose core, leaving the released glycan with a single nonreducing end GlcNAc residue [86, 87]. The single GlcNAc residue remaining on the peptide can be further used for site localization [88]. Global enzymatic release of O-glycans is hampered by the lack of an O-glycosidase which releases a broad spectrum of the heterogeneous O-glycosylation; at  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics Clin. Appl. 2015, 9, 368–382

present, O-glycanase is the only known O-glycosidase and has very limited specificity for only the core 1 O-linked glycan [3, 9, 15, 65]. For O-glycans, chemical release by reductive ␤-elimination is the most commonly used method due to its quantitative release of all O-linked glycans without detectable degradation or peeling.

2.2 Glycan derivatization and/or separation Since native glycans do not have any significant specific UV absorbance, derivatization is often performed to enable sensitive detection and subsequent quantitation of the protein released glycans. Several derivatization methods and compounds have been reported and reviewed extensively [41, 44, 69, 89, 90]. Chemical labeling of glycans is usually done at the reactive reducing end of the glycan because of the advantage of labeling each glycan stoichiometrically in a 1:1 ratio. Glycans derivatized with high charge carrying capacity tags may also ionize better (and more uniformly) than native glycans in MS and thus be detected at higher sensitivity [91, 92]. The reducing end derivatives include labels displaying UV/fluorescence features such as 2-AA (2-aminobenzoic acid), 2-AB (2-aminobenzamide), 2-PA (2-aminopyridine) [42, 45, 91], and APTS (8-aminopyrene-1,3,6-trisulfonic acid) [12, 42], and isobaric tandem mass tags [93, 94] and other stable isotope incorporated labels [78, 79, 95]. Permethylation is another derivatization method where all the hydroxyl groups of the component monosaccharides are substituted with a methyl group, making it less hydrophilic and better for ionization [46, 82, 83, 92]. Incomplete [94] and overmethylation [41, 96] may occur however and increase the heterogeneity of the glycan analyte population. Some disadvantages of glycan labeling are the need for a large excess (and the subsequent clean-up) of labeling reagents to induce higher derivatization yields [40, 41, 89, 91]. Harsh reaction-specific conditions such as low pH (pH80⬚C) might cause partial loss of the terminal and rather labile sialic acid residue [41,74,89,91,97]. Permethylation of glycans can also remove potential sialic acid modifications [74] such as O-acetylation that is known to contribute resistance against sialidase activity [97]. The additional glycan clean-up also introduces a risk of encountering significant sample loss and quantitatively skewing the original glycan population [69]. Incomplete derivatization can also create a bias in the detected glycan population and lower the sensitivity of the MS-based quantitation by splitting the signals of the individual glycan species over multiple analyte compounds. Separation of glycans in an MS off- or on-line conjugated manner can be performed by a broad range of chromatographic and electrophoretic methods by exploiting the anionic or hydrophilic nature of the glycans [12,41,42,46,75,80,98,99]. Some of the common separation techniques include aqueous normal phase (also known as HILIC) LC, weak anion exchange LC, and CE. These methods crudely separate glycans www.clinical.proteomics-journal.com

Proteomics Clin. Appl. 2015, 9, 368–382

373

Figure 3. Generic workflow outlining the common experimental approaches for glycoprofiling at the three glyco-analyte levels, i.e. glycan, glycopeptide, and glycoprotein and when relative and absolute quantitation is typically applied.

based on their molecular size/mass (number of monosaccharides) and/or charge and may require orthogonal separation techniques in order to distinguish isobaric glycan isoforms. Porous graphitized carbon (PGC) is another popular LC separation method for glycomics as it has the ability to separate glycans by their structural conformation as well as their physicochemical properties [75, 100, 101]. Native glycans can efficiently be separated by PGC, which reduces the need for glycan derivatization and linkage- and topology-isomers such as triantennary and biantennary bisecting GlcNAc N-glycans (Fig. 1) can be well resolved. Furthermore, the mobile phases used for PGC are compatible with on-line MS for the sensitive detection and quantitation of the separated glycans. It is of interest to note that while CE-based separation methods are unable to distinguish glycan linkage isomers unlike PGC, the amount of time required for each sample run is shorter (60 min), and can be performed with on-line MS, facilitating faster glycome profiling [12, 40, 42, 81, 102]. In addition to the obvious benefits of identifying the complete set of glycan isomers, efficient off- or on-line chromatographic separation of glycans prior to MS is desirable for less potential ion suppression and for more accurate MS-based quantitative glycoprofiling.

2.3 Glycan structure determination Glycan structure determination can be achieved by several analytical methods. The use of parallel exoglycosidase treatment is often coupled with multiple UV/fluorescence HPLC/CE runs of the same sample. Sequential digestion of the glycans and their resulting retention time shifts in the chromatogram due to their truncation by linkage-specific exoglycosidase action enables unambiguous identification of the glycan linkages and topologies [9, 17, 103]. Similarly, glycan visualization methods by means of fluorescently labeled lectins and  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

antibodies is also dependent on the degree of specificity and the combination of lectins/antibodies used for the staining [8, 23, 30, 104]. As discussed above, detailed structural identification of glycans is a prerequisite for accurate quantitation. The use of MS for glycan structural elucidation has become the preferred method in recent years [99]. Tandem MS fragmentation provides a vast amount of information on the glycan composition, linkage and substructures/determinants of known and unknown glycans and the sensitivity is at least as high as that of UV/fluorescence HPLC/CE analysis of labeled glycans [99]. As a consequence, in-depth characterization of the target glycome is usually provided in a single LC/CE-MS/MS analysis [41, 42, 46, 47, 81, 87, 102, 105]. However, it is important to stress that the MS ionization polarity can affect the glycan fragmentation and detection; ionization in positive mode yields higher ion intensities of glycans for better detection, while negative ionization mode generates more cross ring cleavages of glycans that provides unique structural branching information [101, 106, 107]. Permethylated glycans are often analyzed in the positive ionization mode as linkages can be deduced from the nonpermethylated glycosidic bonds, but the interpretation of permethylated glycan fragmentation can be complex as the ion fragments of structural isoforms will be mixed together [69]. On the other hand, PGC separated glycan isoforms are often analyzed in the negative ionization MS mode for better structural analysis [98, 101]. LC/CE column retention times and order of elution can provide additional lines of evidence for glycan identity using previously published information stored in the database repository of characterized glycans (UnicarbKB). Combinations of these different methodologies provide a high level of detail with respect to the overall glycan diversity in the biological material. This knowledge of the global glycoprofile, including their structural isoforms, is crucial for understanding the quantitative alterations in the glycome of the cellular proteins occurring during disease. www.clinical.proteomics-journal.com

374

E. S. X. Moh et al.

Proteomics Clin. Appl. 2015, 9, 368–382

2.4 Glycan quantitation

affect the absolute quantitation as described above. Hence, internal standards of known concentrations are often used to account for such challenges if available. For MALDI-MS methods, the choice of matrix and the crystal formation can also affect the ionization of the glycans [44, 83]. The presence of multiple charge states and adducts of the same glycan, which all must be considered for quantitation purposes, can furthermore complicate the quantitation. However, the lack of a complete set of glycan standards for all glycan species of interest seems to pose the biggest analytical challenge at present to absolute quantitation of glycans.

In glycomics, identified glycans are typically quantified based on their UV/fluorescence response (for labeled glycans) or ionization response in MS, after separation by HPLC/CE (Table 1). Here, we will describe some common technical considerations required for absolute and relative quantitation of glycans.

2.5 Absolute quantitation by UV/fluorescence HPLC/CE As discussed above, glycans have no intrinsic spectroscopic properties and require derivatization with a UV/fluorescence label for detection and quantitation by spectroscopic methods [41, 89]. By employing reducing-end labeling strategies, the 1:1 (glycan/label) ratio enables direct conversion of the observed UV/fluorescence signals to absolute molar abundances when used with a calibration standard [42, 92]. However, labeling bias and sample loss during additional clean up procedures may occur and there are insufficient pure glycan structural standards for complete standardization of different glycomes. Labeled lectins and antibodies are sometimes used for absolute quantitation of recognized glycan substructures in glycomics as they can be used in an ELISA- or array-type format with calibration standards; high-throughput screening of multiple sample sets can be performed quickly and concurrently with these techniques [8, 30, 35, 104, 108, 109]. Quantitation in this format is based on the abundance of specific glycan features instead of individual intact glycans, making it more suitable for crude mapping the glyco-epitope landscape rather than performing accurate quantitative glycan profiling. This is informative in glycobiology where the distribution of recognition sites of the possible binding or interacting molecules is investigated; however, as some lectins can recognize multiple epitopes, and often require multivalency to provide sufficient affinity, an inaccurate representation of the glyco-epitope quantities may be obtained by such approaches [23].

2.6 Absolute quantitation by MS Advances in MS in recent years have facilitated the further development of MS-based glycomics analysis. The high sensitivity of modern mass spectrometers enables detection of glycans at low-femtomole quantities under favorable conditions [99]. However, MS-based absolute quantitation methods suffer from the fact that glycans may ionize slightly differently from each other resulting in a misrepresentation of some glycans relative to their true molar abundances [101, 105]. Derivatization of glycans has been reported to improve (and perhaps enable more uniform) ionization efficiencies [83, 92], although the sample loss from these labeling procedures and the incomplete derivatization can  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

2.7 Relative quantitation by UV/fluorescence HPLC/CE Relative quantitation by UV/fluorescence HPLC/CE is performed by directly comparing the relative HPLC peak areas corresponding to the observed derivatized glycan species since this reflects accurately their relative quantities. Intersample glycoprofile comparisons can be performed as well, using internal reference peaks to correct for slight variations in the derivatization efficiency, retention time, and detector gain between LC/CE-runs. Sample multiplexing can circumvent this issue by using chemical labels that have different spectrometric properties (e.g. 2-aminobenzamide, aniline, 2-aminoacridone) on separate samples that are then mixed together prior to analysis, though the effect on retention time (and MS/MS fragmentation) need to be taken into account when selecting such labels [45].

2.8 Relative quantitation by MS Relative quantitation by LC/CE-MS-based methods of glycan analysis can be accomplished using several strategies. Isolation of specific target m/z’s corresponding to the mass of the glycans of interest in the chromatogram can be performed postacquisition of the raw MS1 data. The peak areas of such extracted ion chromatograms (EIC) can be summed over the range of identified glycans and their relative abundances determined and represented either as a percentage of the total glycome EIC areas [15,16] or normalized against the most intense peak [23]. This EIC-based measure of analyte abundance is similar to peptide quantitation, which, however, usually is not used as a relative measure to other peptides within the same sample. Direct comparison of the relative intensities of the glycans between samples is accurate since the same glycans will (theoretically) have identical ionization efficiencies in the multiple runs. Minor inaccuracies might occur due to inconsistencies in the experimental run; for example even small variations in the glycan elution pattern may slightly skew the relative ionization strengths. To overcome such issues, multiplexing of multiple biological samples can be performed using isotopic labels or tandem mass tags incorporated during the glycan release process, either on the www.clinical.proteomics-journal.com

375

Proteomics Clin. Appl. 2015, 9, 368–382

Table 1. Advantages and limitations of absolute and relative quantitation by UV/fluorescence HPLC and MS

Type of quantitation

Signal detection method used for quantitation method

Advantages

Disadvantages

References

Absolute quantitation

UV/Fluorescence Calibration curves set-up to correlate observed intensity with absolute quantity

1:1 (label/glycan) conjugation ratio enables direct correlation between detected intensity and glycan concentration Lectin/antibody-based ELISAs and arrays enable fast high-throughput targeted analysis

[30, 39, 42, 92, 108]

MS Use of known internal glycan standards

Information-rich structural characterization by MS/MS High sensitivity that can detect subfemtomole quantity tandem mass tags and stable isotope labels can be incorporated for sample multiplexing Multiplexing with probes of different spectroscopic properties is possible Quick inter- and intra sample glycan comparison

Labeling requires large amount of excess labeling reagent to reduce derivatization inequalities. Loss of modifications of the glycans (acetylation, methylation) More experimental steps required for removal of excess labeling reagents may cause sample loss Multiple epitopes on the same glycan might be under-represented by lectins/antibodies Requires derivatization and potential desialylation Lack of full set of glycan standards as internal standards Potential differential ionization efficiencies of glycans Labeling requires large amount of excess labeling reagent to reduce derivatization inequalities; Loss of modifications of the glycans (acetylation, methylation) More experimental steps required for the removal of excess labeling reagents may cause sample loss Larger and charged glycans might be under represented due to poor ionization efficiency Presence of multiple charge states and adducts of the same precursor mass can complicate quantitation Incomplete derivatization (permethylation, mass tags) will result in poor resolution and inaccurate identification and quantitation Small mass differences between stable isotopic labels require high mass resolution and accurate mass spectrometers

[42, 45, 66, 80, 91, 99, 109]

Relative quantitation

UV/fluorescence Direct peak area comparison between glycans

MS quantitation based on observed ion intensity and can be compared by: 1. Relative peak intensity 2. Normalized intensity against highest peak 3. Normalized spectral abundance 4. Intensity ratio between isotopic labels or tandem mass tag reporters

Label-free underivatized glycans can be analyzed MALDI-MS methodology may bypass LC-separation methods for faster data acquisition MS/MS fragmentation can provide information for detailed structural characterization of glycan topology and linkage. Multiplexing using stable isotopes, isobaric tandem mass tags for direct sample comparison in the same experimental run. Extracted ion chromatogram can distinguish closely eluting isobaric glycans isomers for quantitation

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[83, 92]

[78, 79, 82, 83, 93– 95, 110]

www.clinical.proteomics-journal.com

376

E. S. X. Moh et al.

reducing terminus (e.g. 13 C, or 15 N hydrazide) [93–95, 110] or on the hydroxyl groups (e.g. 13 CH3 I or 12 CH2 DI permethylation) [82]. Such labeling enables direct relative comparison of the glycan abundance between samples that are mixed together and then analyzed within the same LC-MS/MS run.

3

Strengths and limitations of relative or absolute quantitation in disease glycomics

Relative quantitation of the glycan species within a given protein mixture is a widely used method in glycomics to establish important knowledge of the regulation of protein glycosylation and to find potential glycan biomarkers. By quantitatively establishing the relative distribution of glycans as a percentage or fraction of the total complement of glycans within a sample and comparing these distributions between multiple biological samples of different origins, valuable information on the glycosylation machinery can be gained and specific glycan structural differences identified. The enzymatic substrate-product relatedness between the glycan species in the glycan population produced by a cell, and the fact that the mammalian glycosylation machinery has been described can be used to understand how glycan alterations in the glycomic profile are derived from an altered glycosylation machinery. This is a unique feature of glycomics that is not possible in proteomics where the analytes (peptides) are not enzymatically connected in substrate-product relationships. Mapping the alterations of the glycosylation machinery is, in turn, valuable for glycobiologists aiming to understand the molecular and cellular deregulation of the mechanisms relating to protein glycosylation in the context of diseases or other nonphysiological/nonhomeostatic conditions of the cell. In addition, glycomics analysis using relative quantitation may be useful to identify and mechanistically explain the presentation of unique glyco-epitopes, glycans, or glycan subsets within the global glycomein-specific pathophysiological conditions, which, thus, could serve as potential glyco-based disease markers. Taken together, the relative quantitation of protein released glycans is a valuable technique in disease glycomics. As glycomics technologies have matured in recent years, there have been some efforts toward developing methods for absolute quantitation of glycans in glycomics. The interest for absolute quantitation of glycans may have been derived from the proteomics field where there has been a strong desire to know the absolute molar amounts of peptides in order to estimate the exact quantity of the corresponding proteins within a sample. One could ask what increase in information, when the relative quantitation is known, is gained by knowing how much of each glycan (in molar amounts) is present in a set of released glycans without knowing the quantity of the attached protein? Here then, we raise the provocative question; what are the benefits of absolute quantitation over relative quantitation in glycomics where the glycan analytes are  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics Clin. Appl. 2015, 9, 368–382

enzymatically related and where the amount of protein carrier(s) and the site occupancies usually are unknown/ignored parameters when measuring the cell/protein glycomic profile? What type of value (e.g. technical advantage or biological knowledge) would absolute quantitation provide compared to glycomics approaches using relative quantitation? Absolute quantitation methods aim to provide a more exact quantitative analysis than relative quantitation methods by defining the molar amount of the glycan species in a particular sample. However, in many cases in the literature, such as those that label the glycans after release, calibration curves were not developed to allow the translation of the observed intensities into the absolute quantities of the glycans [9, 10, 29, 66, 78–80]. The discussions were instead based on relative quantitation of the glycans by calculation of the signal strength of the individual structures as a fraction of the total signal response, to reflect the relative distribution of the structures rather than a calculation of the absolute amount of each glycan structure. While the absolute molar amount of the glycans provides an accurate measure to evaluate how the total level of glycosylation (glycosylation efficiency) is regulated between samples, similar conclusions can be reached using relative quantitation of isotopically labeled glycans analyzed in the same run or crudely estimated from the LCMS/HPLC signal strength of individually analyzed samples when normalizing for the total protein quantity. This review has discussed several stable isotopic glycan labels or tandem mass tags that enable such information to be obtained without the need to generate time-consuming calibration curves for molar conversion of the glycan species. Most research questions in clinical and disease-focused glycomics appear to be obtaining more important biological information by mapping the expression of specific glycan structures relative to the other structures presented on the protein(s) rather than their absolute molar amounts. This is most likely related to the fact that the absolute molar amount of a glycan does not yield additional insight into a mechanistic explanation of the regulation of protein glycosylation. For example, an increase in the absolute amount of a particular glycan may, as such, arise from one or more sources, which cannot be specified without using orthogonal techniques, e.g. (1) the general upregulation of certain glycosylated proteins that preferentially display that particular glycan structure, (2) increased glycosylation site occupancy of nonregulated proteins, or (3) alterations of the cellular glycosylation machinery in favor of the observed glycan structure. In order to better illustrate these points, consider a theoretical scenario of the glycomics profiling of two relatively simple mixtures of released glycans using absolute and relative quantitation (Table 2). Five of the six glycans were present in both samples and the remaining glycan was unique to Sample A. Absolute quantitation by an internal standard (assuming complete derivatization) of the individual glycans between the two samples shows minor discrepancies between the exact molar quantities and the relative abundances (Table 2, Glycan 2); normalized peak intensity suggests www.clinical.proteomics-journal.com

377

Proteomics Clin. Appl. 2015, 9, 368–382

Table 2. Theoretical scenario of a glycomics analysis comparing absolute and relative quantitation of six hypothetical glycans assuming that they are equally accurate

Absolute quantitation–pmol (% relative abundance)

Relative quantitation– normalized to most intense peak (% relative abundance)

Relative quantitation– stable isotopic labeling or tandem mass tags A:B

Glycan

Sample A

Sample B

Sample A

Sample B

Sample A

Sample B

1 2 3 4 5 6 Total

10 (3.8) 60 (23.1) 35 (13.5) 105 (40.4) 40 (15.4) 10 (3.8) 260 (100%)

10 (4.2) 55 (22.9) 40 (16.7) 80 (33.3) 55 (22.9) 0 (0) 240 (100%)

0.095 (3.8) 0.571 (23.1) 0.333 (13.5) 1 (40.4) 0.381 (15.4) 0.095 (3.8) (100%)

0.125 (4.2) 0.688 (22.9) 0.5 (16.7) 1 (33.3) 0.688 (22.9) 0 (0) (100%)

1 1 1 1 1 1

1 0.917 1.140 0.762 1.375 0

Percentage relative abundance of each glycan is calculated with respect to the total signal intensity of the sample. Glycans 1–6 are related species and of increasing mass (and monosaccharide composition) where the smaller glycan is the precursor of the subsequent larger glycan (glycan 1 is the precursor of glycan 2; glycan 2 is the precursor of glycan 3 and so on).

higher abundance of Glycan 2 in sample B (0.688) than sample A (0.571), but % relative abundance suggests higher abundance of Glycan 2 in sample A (23.1%) than sample B (22.9%). While the small differences seen in absolute amounts of each glycan between the two samples are only measurable when using the absolute quantitation method, technically these differences could also incorrectly be attributed to other experimental factors such as unequal labeling efficiency between samples, inaccurate internal/external standard amounts, differential release efficiencies, differential detection response of individual glycans, and/or small errors in glycoprotein amount in the samples. For this scenario, the unique presence of glycan 6 (largest glycan) in Sample A along with the higher abundance of its biosynthetic precursor (glycan 5) in Sample B, immediately suggests either a further processing by the glycosylation machinery in Sample A relative to Sample B, or an early termination of the glycan processing by the low activity of one or few of the processing enzymes (or the corresponding nucleotide-sugar donors) in Sample B. Relative quantitation obtained by peak normalization against the most intense peak lacks the ability to accurately identify the difference in absolute abundance of the most intense peak; however, this may be crudely estimated from the absolute signal intensity or EIC peak area using this approach (Table 2). The subsequent determination of the relative percentage abundance of each glycan of the total glycan pool illustrates that Sample A shows the same ratios of each structure as the distributions extracted from the absolute quantitation. Recently, developed glycan labels using stable isotopes or tandem mass tags enable direct assessment of the quantitative differences of individual glycans and the total glycosylation, between samples, that can be compared within the same LCMS run. In this theoretical scenario, the relative quantitation approach demonstrates, in the same way as absolute quantitation, that Sample A contains more highly processed glycans or alternatively that the glycosylation pathway was terminated early in Sample B.

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

This hypothetical glycomics scenario illustrates that the same biological conclusions (i.e. glycans are more processed in Sample A than B) can be reached using either relative or absolute quantitation. While knowing the exact glycan quantities can provide additional information such as the total amount of glycosylation on the carrier proteins and how it varies in biology, this additional information holds no significant biological value unless it can be complemented with proteome or glycoproteome data. For example, global glycome differences can be attributed to the regulation of specific glycoforms or to an increase in the glycosylation site occupancy of specific glycoproteins. For clinical glycomics on complex protein mixtures such as those that can be extracted from cell membrane fractions or body fluids, understanding the spatial and temporal regulation of the distribution of each glycan and their specific glycan epitopes is what is important to understand the involvement of protein glycosylation in cell–cell, protein–protein and cell–environment interactions. In our opinion, absolute quantitation of glycans thus becomes useful for the characterization of isolated glycoproteins, e.g. therapeutics, where differences in amount of glycosylation can directly interfere with the protein folding and/or function [111]. However, it should be stressed that such information of the molar glycan amount can similarly be obtained from more protein-centric structural analyses by determining the carrier protein quantity and the glycosylation site occupancy. Hence, unlike proteomics where the absolute amount of each protein is important for the identification of differentially up- or downregulated proteins in disease, relative quantitation in glycomics often gives the same opportunity to identify specific global changes in protein glycosylation in disease. The preference for relative quantitation is demonstrated for the only glycan-based cancer marker currently on the market, i.e. ␣-fetoprotein-L3, which is a core-fucosylated form of ␣-fetoprotein; clinical tests only use the relative abundance of the ␣-fetoprotein-L3 protein with respect to the absolute amount of total ␣-fetoprotein [38,112,113]. In addition

www.clinical.proteomics-journal.com

378

E. S. X. Moh et al.

to the characterization of glycoprotein therapeutics, absolute quantitation of glycans may also have a niche in clinical glycomics of free glycans, e.g. glycans excreted in urine or free oligosaccharides in milk. Defining the exact molar amounts of such carrier-free glycans may have biomarker potential for a number of diseases relating to lysosomal degradation [114]. However, we argue that while absolute quantitation of glycomes may be attractive for some areas of clinical glycomics, at present, relative quantitation of glycans remains the most informative and time/cost-effective method to obtain biological insight into the regulation of the cellular glycosylation machinery and the synthesis of the aberrant glycans that occur in disease.

4

Concluding remarks

In summary then, (1) Knowing the profile of the glycans displayed on the proteins/lipids of a cell (glycomics) can give quantitative information on what epitopes are displayed to the interacting partners such as pathogens, cancer and immune cells. (2) For this purpose, knowing the exact amount of each glycan is not really different from knowing the relative proportions of each glycan in the whole population; the distribution of these structures on the cell surface is reflected equally well by relative and absolute quantitation. (3) The spatial presentation of the glycan on the protein/lipid may affect the density and local heterogeneity of the glycans displayed to the cellular environment. For example, while some glycan features such as the sialyl-Lewisx and fucosylation have been shown to play a role in many specific interactions, currently it is still not understood in detail how the heterogeneity and the spatial and temporal distribution of the glycan population affects these biological interactions. So, if the research question is about how the landscape of glycosylation of the cell affects its function, then obtaining a global glycomic profile based on relative abundance of each glycan should be sufficient to answer many aspects of the carbohydrate-based cellular interactions. However, when imbalances in the amount and/or function of a particular glycoprotein(s) are observed, the research should be directed more to the impact of the heterogeneity of glycosylation at specific sites on the protein. In this case determining the distribution of peptide glycoforms would be informative and would use the application of the established proteomics quantitation methods. To date, however, the technology and software available for glycoproteomic analysis of complex mixtures still benefits from the detailed structural analysis of the released glycans by MS that is yet unable to be carried out at the intact glycopeptide level.  C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

Proteomics Clin. Appl. 2015, 9, 368–382

In conclusion, in this work we argue that relative quantitation of released protein glycans remains the most informative and time effective method of establishing biologically meaningful data on the heterogeneity of protein glycan structures; data that allows glycobiologists to increase their understanding of the mechanisms leading to an altered glycosylation machinery and how the cell interacts with its surrounds. This is commonly performed as a label-free approach using LCMS/MS-based signal intensities, but can equally well be combined with derivatization/labeling techniques. In addition, we argue that knowing the absolute molar amount of single or multiple glycans within a sample holds little additional value without knowing the quantity of the protein/lipid carriers and their site occupancies. Nevertheless, absolute quantitation of glycans may be useful for certain applications, e.g. in the mapping of free (nonprotein carried) oligosaccharides in urine or milk in biomarker discovery studies or when monitoring the glycosylation efficiency on purified recombinant glycoprotein therapeutics. The latter can also sometimes be achieved using protein-centric methodologies. Finally, it should be noted that although the maturation of glycoproteomics is likely to see a continual push toward (glyco)peptide quantitation that has the capacity to provide the identity (and abundance) of the protein carrier, the glycosylation site occupancy and the relative distribution of glycans in a site-specific manner, global glycomics continues to be the approach of choice when detailed analysis of the glycan structures is required to understand the interactions of a cell or protein with its environment. Edward S.X. Moh was funded by the International Macquarie Research Excellence Scholarship. Dr. Morten Thaysen-Andersen was funded by an Early Career Fellowship by Cancer Institute NSW. We thank Dr. Terry Nguyen-Khuong for stimulating discussions relating to quantitative glycomics. The authors have declared no conflict of interest.

5

References [1] Varki, A., Lowe, J. B., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), Biological Roles of Glycans, Essentials of Glycobiology, Cold Spring Harbor, NY 2009, pp. 75–88. [2] Rini, J., Esko, J., Varki, A., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), Glycosyltransferases and Glycan-Processing Enzymes, Essentials of Glycobiology, Cold Spring Harbor, NY 2009, pp. 63–74. [3] Brockhausen, I., Schachter, H., Stanley, P., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), O-GalNAc Glycans, Essentials of Glycobiology, Cold Spring Harbor Laboratory Press: Cold Spring Harbor, NY 2009, pp. 115– 128. [4] Sola, R. J., Griebenow, K., Effects of glycosylation on the stability of protein pharmaceuticals. J. Pharm. Sci. 2009, 98, 1223–1245. [5] Freeze, H. H., Schachter, H., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), Genetic Disorders www.clinical.proteomics-journal.com

Proteomics Clin. Appl. 2015, 9, 368–382

of Glycosylation, Essentials of Glycobiology, Cold Spring Harbor, NY 2009, pp. 585–600. [6] Venkatakrishnan, V., Packer, N., Thaysen-Andersen, M., Host mucin glycosylation plays a role in bacterial adhesion in lungs of individuals with cystic fibrosis. Expert. Rev. Respir. Med. 2013, 7, 553–576. [7] Lee, L. Y., Thaysen-Andersen, M., Baker, M. S., Packer, N. H. et al., Comprehensive N-glycome profiling of cultured human epithelial breast cells identifies unique secretome N-glycosylation signatures enabling tumorigenic subtype classification. J. Proteome Res. 2014, 13, 4783–4795. [8] Sato, Y., Nakata, K., Kato, Y., Shima, M. et al., Early recognition of hepatocellular carcinoma based on altered profiles of alpha-fetoprotein. N. Engl. J. Med. 1993, 328, 1802– 1806. [9] Saldova, R., Struwe, W. B., Wynne, K., Elia, G. et al., Exploring the glycosylation of serum CA125. Int. J .Mol. Sci. 2013, 14, 15636–15654. [10] Gilgunn, S., Conroy, P. J., Saldova, R., Rudd, P. M. et al., Aberrant PSA glycosylation—a sweet predictor of prostate cancer. Nat. Rev. Urol. 2013, 10, 99–107. [11] Varki, A., Kannagi, R., Toole, B. P., Glycosylation Changes in Cancer. 2009. [12] Vanderschaeghe, D., Laroy, W., Sablon, E., Halfon, P. et al., GlycoFibro test is a highly performant liver fibrosis biomarker derived from DNA sequencer-based serum protein glycomics. Mol. Cell Proteomics 2009, 8, 986–994. [13] Sethi, M. K., Thaysen-Andersen, M., Smith, J. T., Baker, M. S. et al., Comparative N-glycan profiling of colorectal cancer cell lines reveals unique bisecting GlcNAc and alpha-2,3-linked sialic acid determinants are associated with membrane proteins of the more metastatic/aggressive cell lines. J Proteome Res. 2014, 13, 277–288. [14] Christiansen, M. N., Chik, J., Lee, L., Anugraham, M. et al., Cell surface protein glycosylation in cancer. Proteomics 2014, 14, 525–546. [15] Chik, J. H., Zhou, J., Moh, E. S., Christopherson, R. et al., Comprehensive glycomics comparison between colon cancer cell cultures and tumours: implications for biomarker studies. J. Proteomics 2014, 108, 146–162. [16] Anugraham, M., Jacob, F., Nixdorf, S., Everest-Dass, A. V. et al., Specific glycosylation of membrane proteins in epithelial ovarian cancer cell lines: glycan structures reflect gene expression and DNA methylation status. Mol. Cell Proteomics 2014, 13, 2213–2232. [17] Arnold, J. N., Saldova, R., Hamid, U. M., Rudd, P. M., Evaluation of the serum N-linked glycome for the diagnosis of cancer and chronic inflammation. Proteomics 2008, 8, 3284–3293. [18] Prince, A., Adhesins and receptors of Pseudomonas aeruginosa associated with infection of the respiratory tract. Microb. Pathog. 1992, 13, 251–260. [19] Jang-Lee, J., Curwen, R. S., Ashton, P. D., Tissot, B. et al., Glycomics analysis of Schistosoma mansoni egg and cercarial secretions. Mol. Cell Proteomics 2007, 6, 1485–1499.

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

379 [20] Rudd, P. M., Elliott, T., Cresswell, P., Wilson, I. A. et al., Glycosylation and the immune system. Science 2001, 291, 2370– 2376. [21] Strugnell, R.A., Wijburg, O. L. C., The role of secretory antibodies in infection immunity. Nat. Rev. Microbiol. 2010, 8, 656–667. [22] Taniguchi, N., Toward cancer biomarker discovery using the glycomics approach. Proteomics 2008, 8, 3205–3208. [23] Pang, P.-C., Chiu, P. C. N., Lee, C.-L., Chang, L.-Y. et al., Human sperm binding is mediated by the Sialyl–Lewisx oligosaccharide on the zona pellucida. Science 2011, 333, 1761–1764. [24] Zhao, Y.-Y., Takahashi, M., Gu, J.-G., Miyoshi, E. et al., Functional roles of N-glycans in cell signaling and cell adhesion in cancer. Cancer Sci. 2008, 99, 1304–1310. [25] Taniguchi, N., Korekane, H., Branched N-glycans and their implications for cell adhesion, signaling and clinical applications for cancer biomarkers and in therapeutics. BMB Rep. 2011, 44, 772–781. [26] Pucic, M., Knezevic, A., Vidic, J., Adamczyk, B. et al., High throughput isolation and glycosylation analysis of IgGvariability and heritability of the IgG glycome in three isolated human populations. Mol. Cell Proteomics 2011, 10, M111 010090. [27] Dimitrov, D. S., Virus entry: molecular mechanisms and biomedical applications. Nat. Rev. Micro. 2004, 2, 109–122. [28] Takahashi, M., Kuroki, Y., Ohtsubo, K., Taniguchi, N., Core fucose and bisecting GlcNAc, the direct modifiers of the Nglycan core: their functions and target proteins. Carbohydr. Res. 2009, 344, 1387–1390. [29] Saldova, R., Fan, Y., Fitzpatrick, J. M., Watson, R. W. et al., Core fucosylation and alpha2-3 sialylation in serum Nglycome is significantly increased in prostate cancer comparing to benign prostate hyperplasia. Glycobiology 2011, 21, 195–205. [30] Wu, J., Zhu, J., Yin, H., Buckanovich, R. J. et al., Analysis of glycan variation on glycoproteins from serum by the reverse lectin-based ELISA assay. J. Proteome Res. 2014, 13, 2197–2204. [31] Lu, J., Isaji, T., Im, S., Fukuda, T. et al., ␤-Galactoside ␣2,6sialyltranferase 1 promotes transforming growth factor-␤mediated epithelial-mesenchymal transition. J. Biol. Chem. 2014, 289, 34627–34641. [32] Felder, M., Kapur, A., Gonzalez-Bosquet, J., Horibata, S. et al., MUC16 (CA125): tumor biomarker to cancer therapy, a work in progress. Mol. Cancer 2014, 13, 129. [33] Ruhaak, L. R., Miyamoto, S., Lebrilla, C. B., Developments in the identification of glycan biomarkers for the detection of cancer. Mol. Cell Proteomics 2013, 12, 846–855. [34] Chen, K., Gentry-Maharaj, A., Burnell, M., Steentoft, C. et al., Microarray glycoprofiling of CA125 improves differential diagnosis of ovarian cancer. J. Proteome Res. 2013, 12, 1408–1418. [35] Saeland, E., Belo, A. I., Mongera, S., vanDie, I. et al., Differential glycosylation of MUC1 and CEACAM5 between

www.clinical.proteomics-journal.com

380

E. S. X. Moh et al.

Proteomics Clin. Appl. 2015, 9, 368–382

normal mucosa and tumour tissue of colon cancer patients. Int. J. Cancer 2012, 131, 117–128.

of solid-phase enriched glycopeptides. Anal. Chem. 2010, 82, 7722–7728.

[36] Leymarie, N., Griffin, P. J., Jonscher, K., Kolarich, D. et al., Interlaboratory study on differential analysis of protein glycosylation by mass spectrometry: the ABRF glycoprotein research multi-institutional study 2012. Mol. Cell Proteomics 2013, 12, 2935–2951.

[51] Zielinska, D. F., Gnad, F., Wisniewski, J. R., Mann, M., Precision mapping of an in vivo N-glycoproteome reveals rigid topological and sequence constraints. Cell 2010, 141, 897– 907.

[37] Arnold, J. N., Saldova, R., Galligan, M. C., Murphy, T. B. et al., Novel glycan biomarkers for the detection of lung cancer. J. Proteome Res. 2011, 10, 1755–1764. [38] Fuzery, A.K., Levin, J., Chan, M. M., Chan, D. W., Translation of proteomic biomarkers into FDA approved cancer diagnostics: issues and challenges. Clin. Proteomics 2013, 10, 13. [39] Clark, G. F., The role of carbohydrate recognition during human sperm-egg binding. Hum. Reprod. 2013, 28, 566– 577. [40] Yodoshi, M., Ikeda, N., Yamaguchi, N., Nagata, M. et al., A novel condition for capillary electrophoretic analysis of reductively aminated saccharides without removal of excess reagents. Electrophoresis 2013, 34, 3198–3205. [41] Harvey, D. J., Derivatization of carbohydrates for analysis by chromatography; electrophoresis and mass spectrometry. J. Chromatogr. B 2011, 879, 1196–1225. [42] Ruhaak, L. R., Hennig, R., Huhn, C., Borowiak, M. et al., Optimized workflow for preparation of APTS-labeled N-glycans allowing high-throughput analysis of human plasma glycomes using 48-channel multiplexed CGE-LIF. J. Proteome Res. 2010, 9, 6655–6664. [43] Kolarich, D., Jensen, P. H., Altmann, F., Packer, N. H., Determination of site-specific glycan heterogeneity on glycoproteins. Nat. Protoc. 2012, 7, 1285–1298. [44] Harvey, D. J., Analysis of carbohydrates and glycoconjugates by matrix-assisted laser desorption/ionization mass spectrometry: an update for 2009–2010. Mass Spectrom Rev. 2014, 9999, 1–155. [45] Knezevic, A., Bones, J., Kracun, S. K., Gornik, O. et al., High throughput plasma N-glycome profiling using multiplexed labelling and UPLC with fluorescence detection. Analyst 2011, 136, 4670–4673. [46] Mechref, Y., Hu, Y., Desantos-Garcia, J. L., Hussein, A. et al., Quantitative glycomic strategies. Mol. Cell Proteomics 2013, 4, 874–884. [47] Patrie, S. M., Roth, M. J., Kohler, J. J., Introduction to glycosylation and mass spectrometry. Methods Mol. Biol. 2013, 951, 1–17. [48] Kaji, H., Isobe, T., Stable isotope labeling of Nglycosylated peptides by enzymatic deglycosylation for mass spectrometry-based glycoproteomics. Methods Mol. Biol. 2013, 951, 217–227. [49] Palmisano, G., Melo-Braga, M. N., Engholm-Keller, K., Parker, B. L. et al., Chemical deamidation: a common pitfall in large-scale N-linked glycoproteomic mass spectrometrybased analyses. J. Proteome Res. 2012, 11, 1949–1957. [50] Shakey, Q., Bates, B., Wu, J., An approach to quantifying N-linked glycoproteins by enzyme-catalyzed 18O3-labeling

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[52] Wang, B., Tsybovsky, Y., Palczewski, K., Chance, M. R., Reliable determination of site-specific in vivo protein N-glycosylation based on collision-induced MS/MS and chromatographic retention time. J. Am. Soc. Mass Spectrom. 2014, 25, 729–741. [53] Kurogochi, M., Matsushista, T., Amano, M., Furukawa, J.-I. et al., Sialic acid-focused quantitative mouse serum glycoproteomics by multiple reaction monitoring assay. Mol. Cell Proteomics 2010, 9, 2354–2368. [54] Mysling, S., Palmisano, G., Hojrup, P., Thaysen-Andersen, M., Utilizing ion-pairing hydrophilic interaction chromatography solid phase extraction for efficient glycopeptide enrichment in glycoproteomics. Anal. Chem. 2010, 82, 5598– 5609. [55] Palmisano, G., Lendal, S. E., Engholm-Keller, K., LethLarsen, R. et al., Selective enrichment of sialic acidcontaining glycopeptides using titanium dioxide chromatography with analysis by HILIC and mass spectrometry. Nat. Protoc. 2010, 5, 1974–1982. [56] Zhang, S., Liu, X., Kang, X., Sun, C. et al., iTRAQ plus 18O: a new technique for target glycoprotein analysis. Talanta 2012, 91, 122–127. [57] Pan, S., Chen, R., Aebersold, R., Brentnall, T. A., Mass Spectrometry based glycoproteomics—from a proteomics perspective. Mol. Cell Proteomics 2011, 10, 1–14. [58] Zhang, Y., Yin, H., Lu, H., Recent progress in quantitative glycoproteomics. Glycoconj. J. 2012, 29, 249–258. [59] Thaysen-Andersen, M., Packer, N. H., Advances in LC-MS/MS-based glycoproteomics: getting closer to system-wide site-specific mapping of the N- and Oglycoproteomes. Biochim. Biophys. Acta 2014, 9, 1437– 1452. [60] Trinidad, J. C., Schoepfer, R., Burlingame, A. L., Medzihradszky, K. F., N- and O-glycosylation in the murine synaptosome. Mol. Cell Proteomics 2013, 12, 3474–3488. [61] Parker, B. L., Thaysen-Andersen, M., Solis, N., Scott, N. E. et al., Site-specific glycan-peptide analysis for determination of N-glycoproteome heterogeneity. J. Proteome Res. 2013, 12, 5791–5800. [62] Stavenhagen, K., Hinneburg, H., Thaysen-Andersen, M., Hartmann, L. et al., Quantitative mapping of glycoprotein micro-heterogeneity and macro-heterogeneity: an evaluation of mass spectrometry signal strengths using synthetic peptides and glycopeptides. J. Mass Spectrom. 2013, 48, 627–639. [63] Thaysen-Andersen, M., Mysling, S., Hojrup, P., Site-specific glycoprofiling of N-linked glycopeptides using MALDITOF MS: strong correlation between signal strength and glycoform quantities. Anal. Chem. 2009, 81, 3933– 3943.

www.clinical.proteomics-journal.com

Proteomics Clin. Appl. 2015, 9, 368–382

[64] Martinez-Aguilar, J., Chik, J., Nicholson, J., Semaan, C. et al., Quantitative mass spectrometry for colorectal cancer proteomics. Proteomics 2013, 7, 42–54. [65] Jensen, P. H., Karlsson, N. G., Kolarich, D., Packer, N. H., Structural analysis of N- and O-glycans released from glycoproteins. Nat. Protoc. 2012, 7, 1299–1310. [66] Royle, L., Campbell, M. P., Radcliffe, C. M., White, D. M. et al., HPLC-based analysis of serum N-glycans on a 96-well plate platform with dedicated database software. Anal. Biochem. 2008, 376, 1–12. [67] Marino, K., Bones, J., Kattla, J. J., Rudd, P. M., A systematic approach to protein glycosylation analysis: a path through the maze. Nat. Chem. Biol. 2010, 6, 713–723. [68] Schiel, J. E., Smith, N. J., Phinney, K. W., Universal proteolysis and MS(n) for N- and O-glycan branching analysis. J. Mass Spectrom. 2013, 48, 533–538. [69] Pabst, M., Altmann, F., Glycan analysis by modern instrumental methods. Proteomics 2011, 11, 631–643. [70] Kyselova, Z., Mechref, Y., Al Bataineh, M. M., Dobrolecki, L. E. et al., Alterations in the serum glycome due to metastatic prostate cancer. J. Proteome Res. 2007, 6, 1822– 1832. [71] Isaji, T., Im, S., Gu, W., Wang, Y. et al., An oncogenic protein golgi phosphoprotein 3 up-regulates cell migration via sialylation. J. Biol. Chem. 2014, 289, 20694–20705. [72] Esko, J. D., Sharon, N., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), Microbial Lectins: Hemagglutinins, Adhesins, and Toxins, Essentials of Glycobiology, Cold Spring Harbor, NY 2009, pp. 489–500. [73] Haselhorst, T., Fleming, F. E., Dyason, J. C., Hartnell, R. D. et al., Sialic acid dependence in rotavirus host cell invasion. Nat Chem Biol. 2009, 5, 91–93. [74] Thaysen-Andersen, M., Larsen, M. R., Packer, N. H., Palmisano, G., Structural analysis of glycoprotein sialylation—part I: pre-LC-MS analytical strategies. Rsc. Adv. 2013, 3, 22683–22705. [75] Palmisano, G., Larsen, M. R., Packer, N. H., ThaysenAndersen, M., Structural analysis of glycoprotein sialylation—part II: LC-MS based detection. Rsc. Adv. 2013, 3, 22706–22726. [76] Dissing-Olesen, L., Thaysen-Andersen, M., Meldgaard, M., Hojrup, P. et al., The function of the human interferon-beta 1a glycan determined in vivo. J. Pharmacol. Exp. Ther. 2008, 326, 338–347. [77] Hildebrandt, H., Dityatev, A., Polysialic acid in brain development and synaptic plasticity. Topics Curr. Chem. 2013, DOI:10.1007/128_2013_446. [78] Walker, S. H., Taylor, A. D., Muddiman, D. C., Individuality normalization when labeling with isotopic glycan hydrazide tags (INLIGHT): a novel glycan-relative quantification strategy. J. Am. Soc. Mass. Spectrom. 2013, 24, 1376– 1384. [79] Bowman, M. J., Zaia, J., Comparative glycomics using a tetraplex stable-isotope coded tag. Anal. Chem. 2010, 82, 3023–3031.

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

381 ˜ [80] Marino, K., Lane, J. A., Abrahams, J. L., Struwe, W. B. et al., Method for milk oligosaccharide profiling by 2aminobenzamide labeling and hydrophilic interaction chromatography. Glycobiology 2011, 21, 1317–1330. [81] Jayo, R. G., Thaysen-Andersen, M., Lindenburg, P. W., Haselberg, R. et al., Simple capillary electrophoresis—mass spectrometry method for complex glycan analysis using a flow-through microvial interface. Anal. Chem. 2014, 86, 6479–6486. [82] Atwood, J. A., Cheng, L., Alvarez-Manilla, G., Warren, N. L. et al., Quantitation by isobaric labeling: applications to glycomics. J. Proteome Res. 2007, 7, 367–374. [83] Jeong, H. J., Kim, Y. G., Yang, Y. H., Kim, B. G., Highthroughput quantitative analysis of total N-glycans by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Chem. 2012, 84, 3453–3460. [84] Song, W., Mentink, R. A., Henquet, M. G., Cordewener, J. H. et al., N-glycan occupancy of arabidopsis N-glycoproteins. J. Proteomics 2013, 93, 343–355. [85] Kita, Y., Miura, Y., Furukawa, J., Nakano, M. et al., Quantitative glycomics of human whole serum glycoproteins based on the standardized protocol for liberating N-glycans. Mol. Cell Proteomics 2007, 6, 1437–1445. [86] Goodfellow, J. J., Baruah, K., Yamamoto, K., Bonomelli, C. et al., An endoglycosidase with alternative glycan specificity allows broadened glycoprotein remodelling. J. Am. Chem. Soc. 2012, 134, 8030–8033. [87] Mulloy, B., Hart, G. W., Stanley, P., in: Varki, A., Cummings, R. D., Esko, J. D., Freeze, H. H. et al. (Eds.), Structural Analysis of Glycans, Essentials of Glycobiology, Cold Spring, Harbor , NY 2009, pp. 661–678 [88] Hagglund, P., Bunkenborg, J., Elortza, F., Jensen, O. N. et al., A new strategy for identification of N-glycosylated proteins and unambiguous assignment of their glycosylation sites using HILIC enrichment and partial deglycosylation. J. Proteome Res. 2004, 3, 556–566. [89] Ruhaak, L. R., Zauner, G., Huhn, C., Bruggink, C. et al., Glycan labeling strategies and their use in identification and quantification. Anal. Bioanal. Chem. 2010, 397, 3457–3481. [90] Miura, Y., Hato, M., Shinohara, Y., Kuramoto, H. et al., BlotGlycoABCTM, an integrated glycoblotting technique for rapid and large scale clinical glycomics. Mol. Cell Proteomics 2008, 7, 370–377. [91] Pabst, M., Kolarich, D., Poltl, G., Dalik, T. et al., Comparison of fluorescent labels for oligosaccharides and introduction of a new postlabeling purification method. Anal. Biochem. 2009, 384, 263–273. [92] Gil, G. C., Kim, Y. G., Kim, B. G., A relative and absolute quantification of neutral N-linked oligosaccharides using modification with carboxymethyl trimethylammonium hydrazide and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Anal. Biochem. 2008, 379, 45–59. [93] Yang, S., Yuan, W., Yang, W., Zhou, J. et al., Glycan analysis by isobaric aldehyde reactive tags and mass spectrometry. Anal. Chem. 2013, 85, 8188–8195.

www.clinical.proteomics-journal.com

382

E. S. X. Moh et al.

Proteomics Clin. Appl. 2015, 9, 368–382

[94] Hahne, H., Neubert, P., Kuhn, K., Etienne, C. et al., Carbonylreactive tandem mass tags for the proteome-wide quantification of N-linked glycans. Anal. Chem. 2012, 84, 3716– 3724.

[104] Klukova, L., Bertok, T., Kasak, P., Tkac, J., Nanoscalecontrolled architecture for the development of ultrasensitive lectin biosensors applicable in glycomics. Anal. Methods 2014, 14, 4922–4931.

[95] Zhang, P., Zhang, Y., Xue, X., Wang, C. et al., Relative quantitation of glycans using stable isotopic labels 1-(d0/d5) phenyl-3-methyl-5-pyrazolone by mass spectrometry. Anal. Biochem. 2011, 418, 1–9.

[105] Marginean, I., Kronewitter, S. R., Moore, R. J., Slysz, G. W. et al., Improving N-glycan coverage using HPLC-MS with electrospray ionization at subambient pressure. Anal. Chem. 2012, 84, 9208–9213.

[96] Robinson, S., Routledge, A., Thomas-Oates, J., Characterisation and proposed origin of mass spectrometric ions observed 30 Th above the ionised molecules of per-Omethylated carbohydrates. Rapid Commun. Mass Spectrom. 2005, 19, 3681–3688.

[106] Harvey, D. J., Dwek, R. A., Rudd, P. M., Determining the structure of glycan moieties by mass spectrometry. Curr. Protoc. Protein Sci. 2006, 43, 12.7.1–12.7.18.

[97] Varki, A., Diaz, S., The release and purification of sialic acids from glycoconjugates: methods to minimize the loss and migration of O-acetyl groups. Anal. Biochem. 1984, 137, 236–247. [98] Melmer, M., Stangler, T., Premstaller, A., Lindner, W., Comparison of hydrophilic-interaction, reversed-phase and porous graphitic carbon chromatography for glycan analysis. J. Chromatogr. A 2011, 1218, 118–123. [99] Wuhrer, M., Koeleman, C. A. M., Hokke, C. H., Deelder, A. M., Nano-scale liquid chromatography-mass spectrometry of 2-aminobenzamide-labeled oligosaccharides at low femtomole sensitivity. Int. J. Mass Spectrom. 2004, 232, 51– 57. [100] Everest-Dass, A. V., Abrahams, J. L., Kolarich, D., Packer, N. H. et al., Structural feature ions for distinguishing N- and O-linked glycan isomers by LC-ESI-IT MS/MS. J. Am. Soc. Mass Spectrom. 2013, 24, 895–906. [101] Pabst, M., Altmann, F., Influence of electrosorption, solvent, temperature, and ion polarity on the performance of LCESI-MS using graphitic carbon for acidic oligosaccharides. Anal. Chem. 2008, 80, 7534–7542. ¨ [102] Balaguer, E., Neususs, C., Glycoprotein characterization combining intact protein and glycan analysis by capillary electrophoresis-electrospray ionizationmass spectrometry. Anal. Chem. 2006, 78, 5384– 5393. [103] Gotz, L., Abrahams, J. L., Mariethoz, J., Rudd, P. M. et al., GlycoDigest: a tool for the targeted use of exoglycosidase digestions in glycan structure determination. Bioinformatics 2014, 21, 3131–3133.

 C 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim

[107] Domon, B., Costello, C., A systematic nomenclature for carbohydrate fragmentations in FAB-MS/MS spectra of glycoconjugates. Glycoconj. J. 1988, 5, 397–409. [108] Yang, H., Li, Z., Wei, X., Huang, R. et al., Detection and discrimination of alpha-fetoprotein with a label-free electrochemical impedance spectroscopy biosensor array based on lectin functionalized carbon nanotubes. Talanta 2013, 111, 62–68. [109] Song, X., Xia, B., Stowell, S. R., Lasanajak, Y. et al., Novel fluorescent glycan microarray strategy reveals ligands for galectins. Chem. Biol. 2009, 16, 36–47. [110] Walker, S. H., Budhathoki-Uprety, J., Novak, B. M., Muddiman, D. C., Stable-isotope labeled hydrophobic hydrazide reagents for the relative quantification of N-linked glycans by electrospray ionization mass spectrometry. Anal. Chem. 2011, 83, 6738–6745. [111] Bowden, T. A., Baruah, K., Coles, C. H., Harvey, D. J. et al., Chemical and structural analysis of an antibody folding intermediate trapped during glycan biosynthesis. J. Am. Chem. Soc. 2012, 134, 17554–17563. [112] Kuzmanov, U., Kosanam, H., Diamandis, E. P., The sweet and sour of serological glycoprotein tumor biomarker quantification. BMC Med. 2013, 11, 31. [113] Hiraoka, A., Nakahara, H., Kawasaki, H., Shimizu, Y. et al., Huge pancreatic acinar cell carcinoma with high levels of AFP and fucosylated AFP (AFP-L3). Intern. Med. 2012, 51, 1341–1349. [114] Bruggink, C., Poorthuis, B. J., Deelder, A. M., Wuhrer, M., Analysis of urinary oligosaccharides in lysosomal storage disorders by capillary high-performance anion-exchange chromatography-mass spectrometry. Anal. Bioanal. Chem. 2012, 403, 1671–1683.

www.clinical.proteomics-journal.com

Relative versus absolute quantitation in disease glycomics.

The glycome of a diagnostic biological material such as blood, urine, saliva, tissue, or cell cultures comprises of a vast array of structurally disti...
551KB Sizes 3 Downloads 10 Views