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. 2025 Feb 20;24(3):1030–1038. doi: 10.1021/acs.jproteome.4c00657

Swift Universal Glycan Acquisition (SUGA) Enables Quantitative Glycan Profiling across Diverse Sample Types

Christopher Ashwood †,‡, Cecilia Voelcker ‡, Richard D Cummings †,*
PMCID: PMC11894644  PMID: 39978775

Abstract

graphic file with name pr4c00657_0005.jpg

The ability to rapidly analyze complex mixtures of glycans derived from glycoproteins is important, but techniques are often laborious and require multiple glycan derivatization steps. Here, we describe an approach termed Swift Universal Glycan Acquisition (SUGA) in which the total released, nonreduced N-glycan samples are analyzed following direct injection and electrospray ionization in a mass spectrometer with a rapid 3 min run time for each sample. As electrospray ionization (ESI) can generate multiple charge states and adducts for the same glycan composition (MS1), deconvolution is performed to yield the relative intensity profile for each detected glycan composition; each annotated composition is supported by an annotated MS2 spectrum. This combination of MS1 and MS2 data enables confident glycan identification. The data obtained by SUGA are comparable to those obtained using permethylated N-glycans analyzed by matrix-assisted laser desorption/ionization (MALDI)-MS. The SUGA approach was applied to the analyses of several purified glycoproteins and N-glycans derived from cells and compared to spectra obtained following permethylation and analysis by MALDI-MS. This new approach will facilitate the rapid and high-throughput analysis of N-glycans from diverse biological samples.

Keywords: high-throughput, glycomics, inhibitor screening, time-course, SUGA

Introduction

Glycans are oligosaccharides and polysaccharides linked to proteins (glycoproteins) and lipids (glycolipids), and their characterization is crucial for understanding the role of glycosylation in biological processes and for identifying potential targets for drug development.1 However, analytical procedures to characterize glycans are often complicated. The typical aim of a glycomics experiment is to detect, identify, and quantify the glycans that are present in a given sample. The most common analytical technology utilizes mass spectrometry (MS) because it allows for the precise identification and quantification of released glycans derived from glycoproteins and glycolipids.2 Mass spectrometry can also provide information about the specific types of glycans present on a protein and their relative abundance. As analyses often involve multiple samples in screening and comparative studies, the application of this approach is more limited by throughput rather than technical limitations. Typical modern approaches, while elegant in many ways, employ various types of glycan derivatization and multiple steps in the analytical processes.3−9

High-throughput analysis of glycans released from glycoconjugates has a variety of benefits, including cost savings and feasibility of large cohort studies. As the glycosciences attempt to answer questions in biology that require population-level analyses, the throughput of current data acquisition methods requires further refinement or new approaches must be developed. While approaches such as sample multiplexing exist, current advances are limited to isobaric labeling (6 samples per acquisition10) or multiple stable isotope derivatization steps.11 To maintain pace with high-throughput sample preparation processes in 96 and 384-well plates, acquisition times should be dramatically reduced. For example, the analysis of a 384-well plate of samples on a single instrument should take 3.75 min per sample or less to be completely acquired in a single day. This is not possible with any currently available method that involves analyte separation prior to mass spectrometric detection.

Liquid chromatography (LC) is frequently paired with fluorescent or mass spectrometric detection of glycans owing to its ability to separate interferences from analytes.12−16 Despite the importance of LC for fluorescent detection, where all glycans produce the same signal, most glycan compositions have a unique mass, allowing them to be detected without any prior separation. In addition to throughput, avoiding LC can also allow greater flexibility with sample solvents, and there are no concerns about sample compatibility with the stationary phase and/or separation buffers.17

To date, there is only a single report describing native glycan characterization by flow injection analysis, but it requires a fluorescent labeling step and acquires data in positive mode, which complicates composition confirmation due to glycan structure rearrangement.18 This is contrasted by Shajahan et al.19 whose focus was permethylated glycans analyzed by matrix-assisted laser desorption/ionization (MALDI), with subsequent qualitative flow injection analysis. Here, we describe a novel pipeline for native, nonreduced N-glycan data acquisition and data analysis, enabling the high-throughput analyses for cohort studies, which we have named Swift Universal Glycan Acquisition (SUGA).

Experimental Section

Cell Culture

ExpiCHO-S cells were cultured in ExpiCHO Expression medium (Thermo Fisher, Cat. A2910001) in a humidified 5% CO2 incubator at 37 °C and 100 rpm. Cells were maintained in a T25 flask until log-phase growth was reached (4 × 106/mL). One day before drug treatment, cells were transferred to 6-well plates, 1 × 105 cells per well for a total volume of 2 mL. Glycosylation inhibitors were added to the culture medium at 5 μg/mL in triplicate for each time point. Every 24 h for 4 days, each well was collected and cells were removed from culture media by centrifugation and washed 3× with PBS yielding a cell pellet. Cell counts and viability were monitored upon collection with each treatment having similar final cell counts (∼2.2 × 106/mL). All treated cells were at greater than 90% viability after the full course of treatment.

Lysis, Glycan Release, Purification, and Dissolution for SUGA

Cell lysis and glycan release were performed following the vendor’s protocol for the 96-well plate S-trap, with PNGase-F instead of trypsin as per the vendor’s suggestion for glycomics and as previously published.4 In brief, cells were lysed by probe sonication in 5% SDS, 100 mM TEAB pH 8.5. Following complete lysis, proteins were reduced and alkylated with DTT and IAA, respectively, and proteins were precipitated and purified from the cell lysate by an S-trap using phosphoric acid and methanol precipitation. Following washing to ensure immobilized protein was sufficiently enriched on the filter membrane, 60 μL of PNGase-F digestion solution was added (New England Biolabs, 1 unit, H2O) and incubated at 37 °C for approximately 18 h. For purified glycoprotein glycan profiling, the protocol above was followed from a later starting point, the reduction and alkylation step.

Carbon cleaning was performed as per manufacturer’s protocol (Hypercarb 25 mg × 96-well plate, Thermo Fisher), which includes loading glycan (0.1% trifluoroacetic acid, TFA), washing out salts (0.1% TFA), eluting the glycans (50% acetonitrile, 0.1% TFA), and drying down in a 96-well plate.

Released glycans were resuspended with solvent depending on the sample derivatization status. For native ESI (SUGA), native glycans were dissolved in H2O with 0.1% piperidine. For the platform comparison of SUGA and MALDI for permethylated glycans, half of the permethylated glycans were dissolved in 20% MeCN, 0.1% formic acid for SUGA, and the other half were dissolved in 10 μL of 50% methanol and spotted in a 1:1 ratio with DHB matrix on an MTP 384 polished steel target plate (Bruker Daltonics #8280781).

Data Acquisition

Five μL portion of each sample was automatically infused into an Orbitrap Fusion Lumos Tribrid mass spectrometer (Thermo Fisher) by an autosampler (Ultimate 3000 autosampler, Thermo Fisher) attached to a loading pump (Ultimate 3000 NanoLC Binary pump, Thermo Fisher). For each 3 min method, 5 μL/min of 0.1% formic acid was delivered from the loading pump to transfer the sample plug to an OptaMax NG H-ESI source (Thermo Fisher) interfaced with the mass spectrometer (tubing schematic can be found in Figure S1 and Table S1).

Data were acquired by a three min Data Dependent Acquisition method consisting of an MS1 full scan range from 730–2000 m/z with an orbitrap resolution of 120 K, and subsequent MS2 scans with resonant excitation collision-induced dissociation (CID) for the most intense precursors to obtain a cycle time of 5 s for 10–12 points across the infused sample plug.

MALDI-time-of-flight (MALDI-TOF) MS data was acquired from a Bruker UltrafleXtreme instrument using FlexControl Software in the reflective positive mode. A mass/charge (m/z) range of 1000–6000 m/z was collected. Twenty-five independent manual captures (representing 1000 shots each) were obtained from each sample and averaged to create the final combined spectra file.

Glycan Identification and Quantification

Spectrum visualization was performed with Freestyle 1.8 SP2 (Thermo Fisher Scientific) and FlexAnalysis 3.4 (Bruker) for SUGA and MALDI-TOF data, respectively. Matching of theoretical glycan compositions to identified monoisotopic peaks was performed using Glycomod.20 MS2 were semiautomatically annotated with putative glycan compositions by Glycoworkbench.21 MALDI data were converted with MALDISkyLink (https://github.com/chrashwood/MALDISkyLink), a free, open-source tool for MALDI data compatibility with Skyline. Glycans were quantified by their precursor isotopic envelopes in Skyline, similar to previous work.22,23

Permethylation Completeness Verification

The most abundant N-glycan for each glycoprotein sample was used to verify the completeness of permethylation by Skyline. The chemical formula for each glycan had CH2 subtracted to represent underpermethylation and the isotopic envelopes for both the fully and underpermethylated chemical formulas were integrated. Permethylation completeness was calculated by the “peak area” of the underpermethylated form divided by the sum of the fully permethylated and underpermethylated forms.

Data Availability

The raw MS glycomics data generated in this study have been deposited in the GlycoPOST database24 under accession code https://glycopost.glycosmos.org/entry/GPST000431. All raw data and processed Skyline documents are available on Panorama (https://panoramaweb.org/SUGA.url).25 Protocols for glycomics analysis are publicly available through the National Center for Functional Glycomics (https://research.bidmc.org/ncfg/protocols). The permethylation efficiency Skyline documents are available on Panorama (https://panoramaweb.org/BIDMC%20-%20Glycomics%20Core/MALDISkyLink_ProofOfConcept/).

Results

Overview of the Swift Universal Glycan Acquisition (SUGA) Method

The overall workflow for SUGA is shown in Figure 1, covering the sample preparation (panel A), throughput (panel B), data acquisition (panel C), glycan identification (panel D), and acquired data (panels E–G). In summary, samples of cells, biofluids, and purified proteins are subjected to treatment by PNGase-F treatment to yield purified, native, released N-glycans. These N-glycans were resuspended and directly infused by a loading pump into a mass spectrometer, yielding a raw file composed of intact glycan spectra (MS1) and fragments of those respective glycans (MS2).

Figure 1.

Figure 1

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Overview of Swift Universal Glycan Acquisition (SUGA) by Data Dependent Acquisition Mass Spectrometry (DDA-MS) method. (A) Sample preparation workflow was used to obtain N-glycans for SUGA. (B) Duration of the sample preparation steps. (C) Instrument setup for automated sample injection and data acquisition. (D) Criteria used for high-confidence glycan identification. Native bovine fetuin N-glycans were analyzed by SUGA. (E) Total Ion Chromatogram (TIC). (F) Negative mode precursor ion mass spectrum with monoisotopic masses and ions subjected to fragmentation labeled. (G) Charge deconvoluted precursor ion mass spectrum with the corresponding glycan identifications.

The sample preparation process was streamlined, including only the necessary steps to achieve a glycan profile. Twelve samples subjected to this workflow took 19.5 h for complete preparation from starting samples through to purified glycans ready for injection into the MS, with 1.5 of those hours being hands-on time. The MS method was optimized to give the greatest glycan intensity possible, achieving 3 orders of magnitude glycan detection (Figures S2–S4).

The resulting data from native N-glycans released from fetal bovine serum-derived fetuin are shown in Figure 1, panels E–G. The total 3 min run time includes a 1 min sample plug bracketed by a minute of buffer to limit sample carryover. The mass spectrum resulting from the sample plug contains multiple negatively charged sialylated N-glycans, and all ions with relative intensities equal to or greater than 1% have been subjected to MS2 acquisition. As electrospray ionization (ESI) can generate multiple charge states and adducts for the same glycan composition, deconvolution was performed to yield the relative intensity profile for each glycan composition detected; each annotated composition is supported by an annotated MS2 spectrum. The results of analyzing the N-glycans of fetuin revealed a total of 9 branched and complex structures containing multiple sialic acid residues (Figure 1). These relatively abundant glycans are similar to those reported by others using related MS approaches.26

SUGA Generates Equivalent Spectra to MALDI-TOF

To explore the universality of the approach, five purified glycoproteins covering a range of glycan types including high mannose, complex neutral, and complex sialylated were subjected to the original workflow with the addition of a permethylation step for a portion of the material to allow direct comparison to MALDI-MS spectra. Permethylation was performed for this comparison to reduce in-source fragmentation during MALDI in positive ion mode.11 Deconvolution of ESI mass spectra also has the benefit of being able to directly compare the spectra obtained by MALDI, which generates predominantly singly charged ions.

To compare data generated by MALDI-TOF and our SUGA method, a diverse range of N-glycans from purified glycoproteins were released, permethylated, and analyzed by SUGA (Figure 2, Panel A). For all permethylated N-glycan profiles acquired, the corresponding relative intensity profiles were equivalent. Permethylation reactions were at least 93% complete (Figure S5). As MS2 is collected for each method, we compared the spectra for a disialylated complex N-glycan acquired natively by SUGA, as well as permethylated by MALDI and SUGA, and the SUGA data generated more informative fragmentation spectra owing to the multiply charged precursor ions (Figure S6).

Figure 2.

Figure 2

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Reproduction of MALDI-TOF permethylated glycan profiles by SUGA. (A) Modified sample preparation workflow to obtain permethylated N-glycans. (B–F) Comparison of deconvoluted mass spectra of N-glycans released from a diverse range of purified glycoproteins using SUGA (top) vs MALDI-MS (bottom). (B) human serum IgG. (C) Xolair antibody. (D) Ribonuclease B. (E) Asialofetuin. (F) contains the bovine A1AGP protein.

Evaluation of SUGA Technical Variation Reveals the Importance of Starting Protein Amount

A flow injection analysis method can be more susceptible to technical variation due to the complexity of the sample infused into the MS without prior separation. To evaluate the technical variation of the MS acquisition approach, replicates of ExpiCHO cells were prepared and acquired in a quintuplet (Figure 3, Panel A). Of the 58 N-glycan compositions detected over 2.5 orders of magnitude, only two compositions had a coefficient of variation (CV) greater than 20% (Figure 3, Panel B). The median CV was approximately 10%, and observed glycan intensity was negatively correlated with the coefficient of variation (Figure 3, Panel C).

Figure 3.

Figure 3

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Dynamic range and technical variation of SUGA. Assessment of data acquisition variation. (A) Experimental workflow was used to assess MS variation only. (B) Distribution of glycan intensity over 3 orders of magnitude dynamic range. (C) Scatter plot of identified glycans showing the relationship between intensity and technical variation. Assessment of sample preparation and data acquisition variation. (D) Experimental workflow to assess total variation. (E) Distribution of glycan intensities across each amount of starting protein for glycan release. (F) Bar chart demonstrates lower technical variation with greater amounts of starting protein.

The starting amount of material for a given glycomics experiment is dependent on the degree to which samples are glycosylated, and as we have shown that MS variation is correlated with glycan intensity, we assessed the impact of the starting amount of cell lysate protein for our SUGA method (Figure 3, Panel D). As shown in Figure 3, Panel E, an expected decrease in glycan intensity was observed as the starting amount of protein decreased. In addition to the decrease in intensity, the glycan composition ranks became discordant from larger sample amounts when the amount of starting protein was less than 50 μg (Figure 3, Panel E). This can be explained by the increasing number of glycans with CVs greater than 20% at 10 and 25 μg of starting protein (Figure 3, Panel F).

Application of the Optimized SUGA Method for Unbiased Glycosylation Inhibitor Screening

With the method optimized for complex cell lysates, the quantitative capacity of the SUGA method was evaluated by the treatment of ExpiCHO cells with varying N-glycosylation inhibitors. Castanospermine, an inhibitor of α-glucosidase involved in the processing of N-glycans,27 significantly increased the amount of di- and monoglucosylated high mannose N-glycans (Figure 4A,B). One limitation observed was the inability to specifically quantify the triglucosylated N-glycan as one of the most abundant sialylated N-glycans is isobaric (Figure S7). Despite this limitation, the results observed were consistent with the mode of action of castanospermine, leading to accumulation of high mannose N-glycans.

Figure 4.

Figure 4

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Application of SUGA for high-throughput assessment of glycosylation inhibitors. (A) Glucosidase inhibition by castanospermine. Expected impact on the glucosidase pathway. (B) Volcano plot demonstrating an increase in glucosylated N-glycan compositions. (C) α1,2-mannosidase inhibition by kifunensine. Expected impact on α1,2 mannosidase pathway. (D) Volcano plot demonstrating an expected increase in oligomannose N-glycans and the corresponding decreases. (E) Temporally resolved glycan abundance changes resulting from inhibition. (F) α1,3/6 mannosidase inhibition by swainsonine. Expected impact on nonlinear N-glycosylation pathway. (G) Volcano plot demonstrating an expected increase in oligomannose and hybrid N-glycans and corresponding decreases in oligo- and pauci-mannose N-glycans. (H) Temporally resolved glycan abundance changes identify differential timing of impact on the pathway.

Evaluating the inhibition of the next step of the biosynthetic pathway, removal of α1,2 mannose, we explore the effects of kifunensine, an inhibitor of α1,2 mannosidases, in N-glycan structures. Kifunensine treatment of cells resulted in a significant increase in several N-glycans including the high mannose glycans expected to increase (Figure 4C,D). Demonstrating the unbiased nature of our acquisition method, we also detected decreases in a few compositions including a hybrid, core-fucosylated N-glycan, and a trisialylated N-glycan. As this inhibitor treatment was a time-course study, it revealed that the ExpiCHO cell line N-glycosylation profile stabilizes after 72 h, with days 3 and 4 showing the greatest increase in the large high-mannose compositions (Figure 4E). These results were also consistent with the mode of action of kifunensine and illuminated which glycan compositions decreased resulting from this treatment, which could not be predicted as it is dependent on the glycosylation machinery of the host cell.28

For a final assessment of quantitative capacity, ExpiCHO cells were treated with swainsonine, a broadly active inhibitor of processing α1,3 and α1,6 mannosidases for N-glycans (Figure 4F).29 In addition to the expected decreases in the N-glycan compositions produced by the drug, new hybrid glycan compositions were observed to increase with the length of time for swainsonine treatment (Figure 4G). Unlike the previous inhibitors, the N-glycosylation pathway can continue through sialylation using those glycan compositions. The time-course treatment revealed that Day 2 resulted in a steady state for the high mannose and pauci-mannose structures, but the hybrid structures appeared to have steady states dependent on the stage of glycosylation processing, with monoantennary sialylation reaching its maxima on Day 3 whereas biantennary sialylation continues to grow up to Day 4 (Figure 4H).

Discussion

Here, we present the SUGA (Swift Universal Glycan Analysis) method, a novel workflow for glycomic analysis that includes streamlined sample preparation, efficient data acquisition, and robust glycan identification. The SUGA method allows for the profiling of native N-glycans from various sources, including cells, biofluids, and purified proteins, using mass spectrometry-based techniques. The results demonstrate the effectiveness and versatility of the SUGA method, its equivalence to MALDI-TOF analysis, and its suitability for unbiased glycosylation inhibitor screening.

Our approach addresses a key bottleneck in the high-throughput analyses of glycans, namely the usual need derivatize glycans in some manner, such as by permethylation or addition of tags.3−7,9,18,30−38 By contrast, in this method, unmodified glycans are used following release from biological samples by PNGase-F treatment. The SUGA workflow starts with sample preparation, where the necessary steps are optimized to efficiently yield purified, native, released N-glycans. For this and most methods, efficient sample preparation is critical for large-scale glycomics studies, where numerous samples need to be processed quickly. Reduction is often a necessary sample preparation step for LC-MS studies due to anomeric splitting of nonreduced glycans;39 however, as LC is skipped in SUGA, this step is not necessary. By directly infusing the resuspended N-glycans into the mass spectrometer using a loading pump, the workflow achieves a rapid 3 min run time for each sample and the resulting mass spectra provide intact glycan information (MS1) and allow the acquisition of their respective fragments (MS2). This combination of MS1 and MS2 data enables confident glycan identification.

Our results also demonstrate the application of the optimized SUGA method for unbiased glycosylation inhibitor screening based on changes in the cellular glycome upon treatment with drugs that perturb glycosylation. A number of such drugs have been identified, and each has a relatively unique target and effect on the outcome of glycosylation.40−43 Such drugs have enormous potential in treatments of viral infections, such as SARS-CoV-2 and HIV,41,44−46 and other disorders.47 In our studies, the treatment of ExpiCHO cells with various N-glycosylation inhibitors reveals significant changes in glycan profiles consistent with the known modes of action of these inhibitors. The SUGA method accurately captures the alterations in glycan composition induced by the inhibitors, providing valuable insights into the glycosylation machinery of the host cells. Additionally, the time-course studies further elucidate the dynamics of glycan processing in response to inhibitor treatment, highlighting the versatility and quantitative capacity of the SUGA method.

The screening of glycosylation pathway manipulation through both chemical and genetic intervention is inherently challenging due to the interconnected nature of the glycan biosynthetic pathway. The SUGA method presents a relatively unbiased approach for N-glycan to better allow high-throughput characterization of the cellular effects of existing or newly developed approaches. There are limitations to this approach, as currently, it is limited to composition only which prevents high-resolution structural definition for assaying isomeric differences, e.g., sialic acid linkages. In addition, the current sample workflow is optimized for assaying N-glycosylation; however, modification of the release and cleanup methods could allow this approach to be used for other aspects of glycosylation including glycosaminoglycans, O-linked glycans, and glycolipids.

In conclusion, the SUGA method presents a powerful and streamlined workflow for comprehensive glycomics analysis. It combines efficient sample preparation, rapid data acquisition, and robust glycan identification, making it suitable for a wide range of glycomics studies. The equivalence of results using either SUGA or MALDI-TOF and the low technical variation in SUGA validate its reliability, repeatability, and throughput. Moreover, the SUGA method is valuable for unbiased glycosylation inhibitor screening, enabling the identification of glycan alterations in response to inhibitor treatments and shedding light on the dynamics of glycan processing. Overall, SUGA offers a promising approach for glycomics research and opens new avenues for understanding the role of glycans in various biological processes and disease states.

Acknowledgments

The authors thank Jamie Heimburg-Molinaro for critical reading and manuscript editing.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jproteome.4c00657.

  • Parts needed for the SUGA reconfiguration of a proteomics application nanoLC (Table S1); configuration for SUGA on a proteomics nanoLC (Figure S1); relationship between AGC % and observed glycan peak area for dextran ladder repeats 5–20 (Figure S2); relationship between observed glycan peak area and orbitrap resolution settings (Figure S3); dynamic range of analytical methods applied to acid-hydrolyzed dextran (Figure S4); quality control of permethylation reaction completeness, (A) bioinformatic pipeline to measure permethylation efficiency, (B) example of skyline data with full- and under-permethylation, (C) sample-specific permethylation efficiency values (Figure S5); comparison of MS2 spectra generated by the same glycan composition in both permethylated and native forms, analyzed by positive and negative mode, respectively (Figure S6); challenge of isobaric N-glycans by the theoretical MS1 isotopic distribution of a sialylated, fucosylated, biantennary complex N-glycan and triglucosylated oligomannose N-glycan at 25 ppm resolution (Figure S7) (PDF)

Author Present Address

§ Protea Glycosciences Pty Ltd., Wollongong, NSW 2500, Australia

Author Present Address

∥ Nuvisan, Biot 06410, Provence-Alpes-Côte d’Azur, France

Author Contributions

C.A. and R.D.C. conceived the concept and wrote the manuscript; C.A. and C.V. conducted experiments; C.A. performed data analysis; R.D.C. supervised the study and acquired funding; all authors reviewed and edited the manuscript.

This work was supported by NIH Grants to R.D.C. (R01AG062181 and R24GM137763).

The authors declare no competing financial interest.

Supplementary Material

pr4c00657_si_001.pdf (496.3KB, pdf)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

pr4c00657_si_001.pdf (496.3KB, pdf)

Data Availability Statement

The raw MS glycomics data generated in this study have been deposited in the GlycoPOST database24 under accession code https://glycopost.glycosmos.org/entry/GPST000431. All raw data and processed Skyline documents are available on Panorama (https://panoramaweb.org/SUGA.url).25 Protocols for glycomics analysis are publicly available through the National Center for Functional Glycomics (https://research.bidmc.org/ncfg/protocols). The permethylation efficiency Skyline documents are available on Panorama (https://panoramaweb.org/BIDMC%20-%20Glycomics%20Core/MALDISkyLink_ProofOfConcept/).

Articles from Journal of Proteome Research are provided here courtesy of American Chemical Society

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