Method Article

Glycomics-Guided Glycoproteomics Facilitates Comprehensive Profiling of the Glycoproteome in Complex Tumor Microenvironments

DOI:

10.3791/67405

⸱

February 7th, 2025

 ,  ,  ,  , 
1School of Natural Sciences, Faculty of Science and Engineering, Macquarie University, 2Institute for Glyco-core Research (iGCORE), Nagoya University

In This Article

Summary

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This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology that offers comprehensive insights into the heterogeneous glycoproteome in complex tumor microenvironments required to better understand the glycobiology of cancers.

Abstract

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Glycosylation is a common and structurally diverse protein modification that impacts a wide range of tumorigenic processes. Mass spectrometry-driven glycomics and glycoproteomics have emerged as powerful approaches to analyze liberated glycans and intact glycopeptides, respectively, offering a deeper understanding of the heterogeneous glycoproteome in the tumor microenvironment (TME). This protocol details the glycomics-guided glycoproteomics method, an integrated omics technology, which firstly employs porous graphitized carbon-LC-MS/MS-based glycomics to elucidate the glycan structures and their quantitative distribution in the glycome of tumor tissues, cell populations, or bodily fluids being investigated. This allows for a comparative glycomics analysis to identify altered glycosylation across patient groups, disease stages, or conditions, and, importantly, serves to enhance the downstream glycoproteomics analysis of the same sample(s) by creating a library of known glycan structures, thus reducing the data search time and the glycoprotein misidentification rate. Focusing on N-glycoproteome profiling, the sample preparation steps for the glycomics-guided glycoproteomics method are detailed in this protocol, and key aspects of the data collection and analysis are discussed. The glycomics-guided glycoproteomics method provides quantitative information on the glycoproteins present in the TME and their glycosylation sites, site occupancy, and site-specific glycan structures. Representative results are presented from formalin-fixed paraffin-embedded tumor tissues from colorectal cancer patients, demonstrating that the method is sensitive and compatible with tissue sections commonly found in biobanks. Glycomics-guided glycoproteomics, therefore, offers a comprehensive view into the heterogeneity and dynamics of the glycoproteome in complex TMEs, generating robust biochemical data required to better understand the glycobiology of cancers.

Introduction

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Protein glycosylation is a prevalent and complex type of co- and post-translational modification of proteins produced by species across the phylogenetic tree of life1,2,3. The protein-linked glycans are known to impact a wide span of biological processes important for human health, including mediation and regulation of cellular interactions and communication events4,5. Aberrant protein glycosylation has been thought to be a cause of malignant transformation, tumor progression, and spread6,7,8. This implicates glycans in key tumorigenic processes in the tumor microenvironment (TME) and offers a considerable and largely untapped potential for the discovery of glycan-based biomarkers and therapeutic applications. Glycobiology is, therefore, receiving increasing attention in cancer research and across life science9.

Powered by key developments in glycoanalytics and informatics over the past decade10,11,12,13,14,15,16,17, liquid chromatography-tandem mass spectrometry (LC-MS/MS)-driven glycomics and glycoproteomics have been established as powerful tools for the system-wide profiling of liberated glycans and intact glycopeptides from complex mixtures of glycoproteins extracted directly from their biological sample origins such as various types of cancer patient specimens (e.g., tumor tissues, cell populations, bodily fluids)3,13,18,19,20.

Glycomics provides information on the structure and quantity of the glycan repertoire (the glycome), which is dynamically produced by cells, tissues, and even entire organisms. On the other hand, glycoproteomics provides quantitative information about the protein carriers and their site(s) of glycosylation, the site occupancy rates (macro-heterogeneity), the site-specific glycan compositions and their site distribution pattern (micro-heterogeneity) with limited glycan structural information (often limited to glycan composition)15,16,21. Hence, glycomics and glycoproteomics are complementary approaches to survey the biochemical details of the glycoproteome22.

A myriad of methodological designs and analytical strategies for glycomics and glycoproteomics have been developed and applied across laboratories depending on the glycoanalyte of interest (e.g., N-/O-/C-linked, glycan/glycopeptide/glycoprotein, labeled/unlabeled), the nature of the sample (e.g., cells, tissues, bodily fluids), amount (nanogram/microgram/milligram protein levels) and the analytical instrument available23,24,25,26,27,28,29,30,31,32. Despite the recognized benefits of their parallel use, glycomics and glycoproteomics are not commonly applied together but rather as stand-alone approaches in glycobiological and cancer research studies due to their high demand for analytical expertise and specialized instrumentation.

Recognizing this technology gap, more than a decade ago, we introduced the glycomics-assisted glycoproteomics method, which is capable of integrating glycomics and glycoproteomics data obtained from complex biological specimens33. In short, the glycomics-assisted (or glycomics-guided as in this protocol) glycoproteomics technology profiles the N- and/or O-glycans through an established porous graphitized carbon (PGC) LC-MS/MS method34,35, which are then used for the analysis of intact N- and/or O-glycopeptide data acquired from the same sample(s) by defining the boundaries of the glycan search space36. PGC-LC-MS/MS is a particularly powerful glycomics method as it provides quantitative insight into the glycome with high sensitivity and structural resolution, yielding information on topology (monosaccharide sequence and branching pattern) and glycosidic linkages of glycans even within complex mixtures35,37,38. The glycoproteomics workflow involves peptide generation, optional peptide labeling, multiplexing, and prefractionation, as well as enrichment before reversed-phase LC-MS/MS analysis that ideally employs different fragmentation methods including stepped collision energy/higher-energy collisional dissociation (sceHCD, for N-glycopeptides) and electron transfer/higher-energy collisional dissociation (EThcD, for O-glycopeptides) for analyte identification. For N-glycoproteome analysis, parallel de-N-glycoproteomics can guide the intact N-glycopeptide data analysis by defining the protein/peptide search space and by providing more details on the occupied N-glycosylation sites and their rate of site occupancy while the parallel analysis of non-modified peptides reveals any protein level changes between the studied conditions.

This paper provides a detailed and easy-to-follow step-by-step protocol for the glycomics-guided glycoproteomics method (see Figure 1 for workflow overview). The protocol provides experimental details of the sample preparation steps and the LC-MS/MS-based glycomics and glycoproteomics experiments and presents representative results demonstrating the application of the method to formalin-fixed paraffin-embedded (FFPE) tissue sections of tumors resected from colorectal cancer (CRC) patients, enabling insights into the glycobiology of the TME in a valuable sample type that is archived in cancer biobanks around the world. The analysis and integration of glycomics and glycoproteomics data are also briefly discussed.

Glycoproteomics workflow diagram; N-glycan release, enrichment, LC-MS/MS analysis setup.
Figure 1: Overview of the glycomics-guided glycoproteomics method. The overview shows detailed experimental steps in glycomics (left) and glycoproteomics (right) workflows, with an overview of the information obtained (bolded) and how the two approaches are integrated through improved data analysis and interpretation. Insert: Home-made SPE micro-columns used for the (i) glycomics and (ii-iii) glycoproteomics workflows. Please click here to view a larger version of this figure.

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Protocol

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The study was approved by the Human Research Ethics Committee (Medical Sciences) at Macquarie University, Sydney, Australia (Protocol 5201800073).

NOTE: The glycomics-guided glycoproteomics method can be applied to a diverse set of biological samples ranging from low to extreme glycoprotein complexity. For label-free glycoproteomics approaches (in which sample pooling is not performed), approximately 100-200 µg total protein from complex mixtures is required as starting material to ensure high glycoproteome coverage after enrichment for glycopeptides that may only constitute 1%-10% of the peptide population. Due to space constraints, the initial protein extraction from cells or tissues is left out, and readers are referred to other resources15. Glycomics conservatively consumes ~10-20 µg protein per analysis for deep glycome profiling, leaving the majority of the protein extract for glycoproteomics. If not stated otherwise, final concentrations have been mentioned, and chemicals should be dissolved in ultra-high-grade water to make aqueous solutions in the protocol below.

1. Protein preparation

  1. Determine extracts' protein concentration using an established method (e.g., bicinchoninic acid or Bradford protein assays). Adjust the protein concentration to 10 µg/µL in 50 mM TEAB (pH 8.5).
  2. Reduce disulfide bonds by incubating samples with 10 mM DTT for 45 min at 56 °C. Alkylate free thiol groups by subsequently incubating samples with 40 mM IAA in the dark for 30 min at 20-25 °C. Quench the alkylation reaction with excess DTT (~40 mM).
  3. Split each protein sample. Allow approximately 10-20 µg total protein for glycomics and 100-200 µg total protein for the glycoproteomics workflow.
    NOTE: In addition to the protein sample(s) of interest, include a known model glycoprotein (e.g., bovine fetuin) or protein mixture (e.g., cell line lysate, plasma) to assess the efficiency and reproducibility of the procedures and to monitor the LC-MS/MS performance.

2. N-glycomics workflow

  1. Immobilization of proteins on a PVDF membrane
    1. Use a single-hole puncher to cut the PVDF membrane fitting with the number of protein samples to be spotted.
    2. Wet the excised membranes with ethanol or methanol, then transfer the membranes to a flat-bottom 96-well plate.
    3. Once the membranes are near-dry, deposit the protein samples in a maximum volume of 2.5 µL per spot to minimize the surface area of the deposited protein sample. Re-spot samples if needed so that the total amount of immobilized protein on the spot is ~10-20 µg. To do this, let the spot air dry before repeating the spotting procedure.
      NOTE: During spotting, re-wetting the membrane with ethanol or methanol may be needed to prevent the membranes from drying to completion.
    4. Air dry the membranes at 20-25 °C for at least 3 h, preferably overnight. Avoid any contamination of the membranes while drying.
      NOTE: As an optional quality control step, the proteins spotted on the membranes can be visualized by staining with Direct Blue 71 solution as previously described34.
  2. Release of protein-linked N-glycans
    1. Add 100 µL of 1% (w/v) PVP40 in methanol to each well containing the spotted membranes. Ensure that the side of the membrane on which the protein sample was spotted is facing upwards throughout the sample preparation.
    2. Shake the plate in the PVP40 solution for 5 min at 20-25 °C, then discard the PVP40 solution.
      NOTE: The PVP40 solution blocks the PVDF membranes and the wells to prevent non-specific binding of PNGase F (and any exoglycosidases that may be applied to achieve linkage-specific information in parallel experiments; see Discussion below) in the subsequent steps.
    3. Wash each well containing the blocked protein spots with 100 µL of ultrapure water and shake the plate for 5 min. Repeat this step 3x. Ensure that the water is completely discarded after each wash.
    4. Add 15 µL of PNGase F solution (0.5 U/µL, diluted in ultrapure water from the 10 U/µL stock solution) to each well, assuming 15 µg protein per sample has been deposited.
    5. Add water to the surrounding empty wells to prevent sample dehydration during the subsequent steps of incubation, and use transparent film to seal the plate carefully. Incubate the plate for at least 8 h at 37 °C.
    6. Sonicate the plate for 5 min to aid any evaporated droplets on the side of the wells to collect at the bottom of the plate.
    7. Transfer the released N-glycan samples carefully to individual microcentrifuge tubes (1.5 mL). Wash the sample wells 2x with 20 µL of ultrapure water, and dispense, and aspirate several times. Combine any remaining sample from each well into the microcentrifuge tubes containing the N-glycan samples.
      NOTE: Avoid using low-binding tubes for the N-glycomics sample preparation, as the hydrophilic glycans may bind irreversibly to the coated tubes.
    8. Add 10 µL of 100 mM ammonium acetate pH 5.0 and incubate the samples for 1 h at 20-25 °C.
      NOTE: This step hydroxylates the aminated N-acetylglucosamine residues on the reducing end of the released N-glycans, allowing an exhaustive reduction in the subsequent step. Adjust 100 mM ammonium acetate to pH 5.0 as high glycosylamine-to-hydroxyl conversion rates in the reducing end of the detached N-glycans depend on the weak acidity of the solution.
    9. Dry the N-glycan samples in a vacuum concentrator system.
  3. Reduction of liberated N-glycans
    1. Add 20 µL of freshly prepared 1 M NaBH4 in 50 mM KOH to the dried N-glycan samples and mix thoroughly.
      NOTE: NaBH4 should be freshly prepared in KOH on the day it is used.
    2. Tightly close the lids of each tube and incubate the samples for 3 h at 50 °C.
      NOTE: N-glycans undergo a reduction, which converts the α- and β-anomers of the reducing end to a single alditol form, leading to a single chromatographic peak in the PGC-LC-MS/MS analysis.
    3. After incubation, spin the sample tubes in a microcentrifuge to bring the liquid to the bottom of the tubes.
    4. Add 100 µL of ultrapure water to each sample. Add 2 µL of glacial acetic acid to neutralize the alkaline N-glycan samples. Spin the sample tubes in a microcentrifuge to bring samples to the bottom of the tubes.
      NOTE: Glacial acetic acid neutralizes the alkaline samples which leads to hydrogen gas (H2) formation and subsequent effervescence during the reaction.
  4. Desalting of N-glycans using PGC-C18-SPE
    1. Prepare home-made PGC-C18-SPE micro-columns as described below.
      1. Using a suitable syringe, plug and transfer C18 disks to suitable pipette tips (10 µL).
        NOTE: Here, commercial C18-SPE micro-columns can also be used.
      2. Pipette 10 µL slurry of PGC resin suspended in 50% (v/v) methanol into the C18-SPE micro-columns. Place the micro-columns in 2 mL tubes fitted with microcentrifuge adapters. Spin the tubes to create PGC-C18-SPE micro-columns with a column height of ~0.5 cm (see Figure 1i). Prepare one PGC-C18-SPE micro-column for each sample.
        NOTE: The use of adapters ensures the micro-columns are suspended in the center of the tube.
      3. For the subsequent washing and sample loading steps, set a benchtop centrifuge to 6,000 rpm (~2,000 x g) and spin for 60 s to facilitate the mobile phases to pass through the PGC-C18-SPE micro-columns. Wash and condition each PGC-C18-SPE micro-column sequentially with 50 µL 0.1% (v/v) TFA in ACN three times followed by 50 µL 0.1% (v/v) TFA three times.
        NOTE: The PGC-SPE micro-columns can be prepared hours in advance, but must be stored hydrated at room temperature prior to usage.
    2. Apply the N-glycan samples to the PGC-C18-SPE micro-columns in a loading volume of up to 40 µL and spin at 6,000 rpm (~2,000 x g) for 60 s after each addition. Repeat the loading step until the entire N-glycan sample has been applied to the micro-column.
      NOTE: While the flowthrough fractions should not contain glycans, we recommend keeping these fractions until the glycomics data has successfully been acquired in the event of incomplete retention on the PGC-C18-SPE micro-columns.
    3. Wash the PGC-C18-SPE micro-columns with 20 µL of ultrapure water. Transfer the PGC-C18-SPE micro-columns to new 1.5 mL microcentrifuge tubes in preparation for N-glycan elution.
    4. Elute the N-glycans by adding 40 µL of 0.1% TFA in 50% (both v/v) ACN to each PGC-C18-SPE micro-column, then spin. Repeat this procedure and combine the eluate.
      NOTE: It is important to monitor the micro-columns after each spin to ensure the solution has been completely spun through, as this will facilitate efficient elution.
    5. Dry the desalted N-glycans in a vacuum concentrator system.
  5. PGC-LC-MS/MS acquisition
    1. Prepare LC solvents as described below.
      1. Solvent A: Prepare 100 mL of 100 mM ammonium bicarbonate (NH4HCO3) stock solution in ultrapure water. Filter the stock solution through a 0.2 µm nylon filter disk using a vacuum filtration unit to remove particulates. Then, prepare 1 L of 10 mM NH4HCO3 solution (solvent A) by diluting 100 mL of 100 mM NH4HCO3 stock solution in 900 mL of ultrapure water. Sonicate solvent A for 15 min to degas the solution. Solvent A can be stored for 1 week at 20-25 °C, while the 100 mM NH4HCO3 stock solution can be stored for 1 month at 4 °C.
      2. Solvent B: Prepare 1 L of 10 mM NH4HCO3 in 70% (v/v) ACN in ultrapure water (solvent B) by adding 100 mL of 100 mM NH4HCO3 stock solution (see previous step 2.5.1.2) to 700 mL of ACN and dilute to a total volume of 1 L with ultrapure water. Sonicate solvent B for 15 min to degas. Solvent B can be stored for 2 weeks at 20-25 °C.
    2. Resuspend the desalted N-glycans in 10 µL of ultrapure water and mix thoroughly. Spin the samples at 14,000 x g for 5 min, then carefully transfer the supernatant to MS sample vials, leaving approximately 0.5 µL behind to avoid transferring any visible or invisible particulates and debris that would damage the LC-MS/MS system.
    3. Inject and separate the N-glycans on a PGC-LC capillary column heated to 50 °C using a 60 min linear (or otherwise appropriate) gradient of 0%-70% (v/v) solvent B in solvent A on a HPLC system. Ensure that the HPLC system is connected to the mass spectrometer and operates with a constant flow rate of 20 µL/min (adjust the flow rate to match the geometry of the PGC-LC column).
    4. Detect the separated N-glycans using a linear ion trap quadrupole mass spectrometer (or another suitable low- or high-resolution MS system) installed with an electrospray ion source and operating in negative ion polarity mode.
    5. Set the MS1 acquisition scan range to m/z 150-2,000 with a zoom scan peak width of m/z 0.25 full-width half maximum (FWHM) at m/z 200. Set the source voltage to 3.2 kV. For MS1 scans, set the automatic gain control (AGC) to 5 x 104 ions with a maximum accumulation time of 50 ms (or otherwise appropriate settings).
      NOTE: The low starting m/z is to include the detection of mono- and di-saccharides that often contribute to the N- and O-glycome.
    6. For MS/MS, set the resolution to m/z 0.25 FWHM at m/z 200, the AGC to 2 x 104 ions, and the maximum accumulation time to 300 ms. Enable data-dependent acquisition (DDA) and select the five most abundant precursors in each MS1 full scan for fragmentation using resonance activation (ion trap) collision-induced dissociation (CID) at a normalized collision energy (NCE) of 33%. Deselect dynamic exclusion to avoid missing closely eluting isobaric N-glycan isomers.
  6. Data analysis of LC-MS/MS glycomics data
    1. Browse the generated LC-MS/MS raw data using appropriate software to ensure high data quality after confirming narrow LC peak width, effective N-glycan isomer separation, high MS accuracy, resolution, and signal-to-noise, and high CID-MS/MS fragmentation efficiency.
    2. Manually identify the N-glycans in the sample based on their monoisotopic precursor mass, CID-MS/MS fragmentation pattern, and relative and absolute PGC-LC retention time.
      NOTE: RawMeat v2.1, GlycoMod, and GlycoWorkBench v2.1 can aid the identification process as described26. Known biosynthetic constraints and structural relatedness amongst the observed analytes can also add further confidence in the identified N-glycans26. Alternatively, semi-automatic glycan identification and spectral annotation can be done using commercial software.
    3. For relative N-glycan quantitation, generate a transition list containing the monoisotopic precursor m/z of all identified N-glycan isomers and use a commercial software to determine the area-under-the-curve (AUC) values based on extracted ion chromatograms (EICs) of all N-glycan precursor ions as described26.

3. N-glycoproteomics workflow

  1. Digestion of protein mixture
    1. Digest 100-200 µg of the reduced and alkylated protein extract (from part 1) by adding trypsin (1:50 enzyme:protein ratio, w/w) and/or other proteases of choice to each sample.
      NOTE: Unlike for the glycomics sample preparation, use, if possible, low binding microcentrifuge tubes for the glycoproteomics sample preparation steps for maximum peptide recovery.
    2. Incubate the samples for 8-12 h at 37 °C. Ensure that the pH is ~8.5 since a mildly alkaline solution improves the trypsin digestion efficiency.
      NOTE: Specific proteases that produce predictable cleavage patterns and leave most N-glycopeptides with a single sequon (glycosylation site), such as trypsin and Lys-C, are preferred. Avoid over-incubating the samples to reduce non-specific cleavages of proteins by trypsin or other proteases.
    3. Stop the digestion by acidification (to avoid non-specific cleavages induced by prolonged digestion) by the addition of 1% (v/v) TFA.
  2. Peptide desalting using Oligo R3-C18-SPE
    1. Prepare home-made Oligo R3-C18-SPE micro-columns as described below.
      1. Using a suitable syringe, plug and transfer C18 disks to 10 µL pipette tips.
        NOTE: Here, commercial C18-SPE micro-columns can also be used.
      2. Deposit a slurry of Oligo R3 resin suspended in neat ACN into the C18-SPE micro-columns. Place the micro-columns in 2 mL tubes fitted with microcentrifuge adapters to ensure the micro-columns are suspended in the center of the tube and spin to create Oligo R3-C18-SPE micro-columns with a column height of ~1 cm (see Figure 1ii). Prepare one Oligo R3-C18-SPE micro-column for each sample.
    2. For the subsequent washing and sample loading steps, adjust a bench top centrifuge to 6,000 rpm (~2,000 x g) and spin for 60 s to allow the mobile phases to pass through the Oligo R3-C18-SPE micro-columns. Wash and condition each Oligo R3-C18-SPE micro-column sequentially with 50 µL of neat ACN, 3x, and then 50 µL of 0.1% (v/v) TFA, 3x.
      NOTE: The Oligo R3-C18-SPE micro-columns can be prepared in advance and stored hydrated for hours at room temperature prior to usage.
    3. Place the conditioned Oligo R3-C18-SPE micro-columns in new 1.5 mL tubes along with microcentrifuge adapters. Apply the tryptic-digested peptide mixtures to the Oligo R3-C18-SPE micro-columns and spin at 2,000 x g for 60 s. Multiple rounds of loading may be required for sample volumes larger than 50 µL.
    4. Wash the Oligo R3-C18-SPE micro-columns with 50 µL of 0.1% (v/v) TFA, 3x. Transfer the Oligo R3-C18-SPE micro-columns to new 1.5 mL tubes in preparation for the elution of the desalted peptides.
    5. Elute the desalted peptides by applying 50 µL of 0.1% TFA in 50% (both v/v) ACN, followed by 50 µL of 0.1% TFA in 70% (both v/v) ACN to each Oligo R3-C18-SPE micro-column. Spin after each step.
      NOTE: It is important to monitor the micro-columns after each spin to ensure the solution has been completely spun through, as this will facilitate efficient elution.
    6. Collect, combine, and dry the desalted peptides in a vacuum concentrator system.
      NOTE: If performing parallel proteomics and de-N-glycoproteomics (see Discussion), split the desalted peptide samples and set aside separate fractions before drying.
  3. Enrichment of N-glycopeptide using ZIC-HILIC-C8-SPE
    1. Prepare home-made ZIC-HILIC-C8-SPE micro-columns as described below.
      1. Using a suitable syringe, plug and transfer C8 disks to 10 µL pipette tips.
      2. Deposit a slurry of ZIC-HILIC resin suspended in methanol into the C8-SPE micro-columns. Place the micro-columns in 2 mL tubes fitted with microcentrifuge adapters to ensure the micro-columns are suspended in the center of the tube and spin to create ZIC-HILIC-C8-SPE micro-columns with a column height of ~1 cm (see Figure 1iii). Prepare one ZIC-HILIC-C8-SPE micro-column for each sample.
    2. For the subsequent washing and sample loading steps, adjust a bench top centrifuge to 6,000 rpm (~2,000 x g) and spin for 60 s to allow the mobile phases to pass through the ZIC-HILIC-C8-SPE micro-columns. Wash and condition each ZIC-HILIC-C8-SPE micro-column sequentially with 50 µL of methanol, 3x, and then 50 µL of ultrapure water, 3x, and finally 50 µL of 1% TFA in 80% (both v/v) ACN, 3x.
      NOTE: The ZIC-HILIC-C8-SPE micro-columns can be prepared hours in advance but must be stored hydrated at room temperature prior to usage.
    3. Resuspend the dried peptide mixtures designated for N-glycopeptide enrichment in 50 µL of 1% TFA in 80% (both v/v) ACN and mix thoroughly.
      NOTE: If peptides are difficult to redissolve in the HILIC loading solvent, the peptides can be firstly redissolved in 10% (v/v) TFA and then quickly diluted in ACN to reach the concentrations of the HILIC loading solvent. Avoid heating the sample while in high TFA concentration to avoid inducing hydrolysis.
    4. Place the conditioned ZIC-HILIC-C8-SPE micro-columns in new 1.5 mL tubes along with microcentrifuge adapters. Apply the resuspended peptide mixtures to the ZIC-HILIC-C8-SPE micro-columns and spin at 2,000 x g for 60 s. Multiple rounds of loading may be required for sample volumes larger than 50 µL.
    5. Collect the flowthrough fraction(s) and re-apply fraction(s) to the same ZIC-HILIC-C8-SPE micro-column. Repeat the re-application 2x and keep the flowthrough tubes.
    6. Wash the ZIC-HILIC-C8-SPE micro-columns 2x with 50 µL of 1% TFA in 80% (both v/v) ACN, spin, and combine this flowthrough with the flowthrough from step 3.3.4.
      NOTE: The combined flowthrough fraction typically contains ~90% of the total peptide material, effectively forming the non-modified peptide fraction that, therefore, can be used for separate downstream analysis (rather than carrying out a separate proteomics type experiment).
    7. Transfer the ZIC-HILIC-C8-SPE micro-columns to new 1.5 mL low-binding microcentrifuge tubes. Elute the retained N-glycopeptides sequentially with 50 µL of 0.1% (v/v) TFA, followed by 50 µL of 25 mM ammonium bicarbonate, and then 50 µL of 50% (v/v) ACN. Spin at 2,000 x g for 60 s after each step.
      NOTE: The eluate typically contains ~10% of the total peptide material, most of which should be N-glycopeptides if high enrichment efficiency is achieved. It is important to monitor the micro-columns after each spin to ensure the solution has been completely spun through, as this will facilitate efficient elution.
    8. Collect, combine, and dry the enriched N-glycopeptides and (if required) the non-modified peptide flow through fractions in a vacuum concentrator system.
  4. Reversed-phased LC-MS/MS of intact N-glycopeptides
    1. Prepare LC solvents as described below.
      1. Solvent A: Prepare 1 L of 0.1% (v/v) formic acid in ultrapure water. Sonicate for 15 min to degas the solvent. Solvent A can be stored for one month at 20-25 °C.
      2. Solvent B: Prepare 1 L of 0.1% formic acid in 99.9% (both v/v) ACN. Sonicate for 15 min to degas. Solvent B can be stored for one month at 20-25 °C.
    2. Resuspend the dried peptide fractions in 0.1% (v/v) formic acid to a concentration of approximately 0.2-0.5 µg peptide/µL and mix thoroughly. Spin the samples at 14,000 x g for 5 min, then carefully transfer the supernatant to MS sample vials, leaving approximately 0.5 µL behind to avoid transferring any unwanted particulates and debris.
    3. For each sample, inject ~0.5-1 µg total peptide on a C18-LC trap column and separate the peptides on a C18-LC analytical column at a constant flow rate of 250 nL/min using a UPHLC System connected to the mass spectrometer with a gradient of 2%-30% solvent B over 100 min, 30%-50% B over 18 min, 50%-95% B over 1 min, and 9 min at 95% (all v/v) B in solvent A (or an otherwise appropriate setup).
    4. Detect the peptides with a high-resolution mass spectrometer coupled to the nanoLC-system equipped with a nano-electrospray ion source and operating in positive ion polarity mode.
    5. For MS1, use an acquisition scan range of m/z 350-1,800, high-resolution at 60,000-120,000 FWHM at m/z 200, and a source voltage of ~2.5 kV. Set the AGC to 3 x 106 ions with a maximum accumulation time of 50 ms (or otherwise appropriate settings).
    6. For MS/MS, select the 20 most abundant precursor ions from each MS1 full scan for fragmentation using DDA mode employing HCD-MS/MS with a fixed NCE of 35% or sceHCD with NCE of 25%, 35% and 45%. Importantly, fix the lower m/z value to 110 (e.g., scan range m/z 110 - 1,800) to observe all the low-mass oxonium ions in each fragment spectrum.
    7. Select precursors that carry more than 2 charges (Z ≥ 2) for fragmentation. Acquire (sce)HCD-MS/MS spectra at 30,000-45,000 FWHM resolution at m/z 200 with an AGC of 1 x 105 ions and 90 ms accumulation time using a precursor isolation window of ± 1.0 Th. Enable dynamic exclusion of 30 s after the single isolation and fragmentation of a given precursor ion.
      NOTE: If ETD is available, consider also acquiring EThcD-MS/MS of intact N-glycopeptide samples using product-dependent ion triggering. Carry out this step when diagnostic glycan oxonium ions (e.g., m/z 138.0545, 204.0867, and 366.1396) are among the top 20 fragment ions within each HCD-MS/MS spectrum39. Detect the EThcD-MS/MS fragments at 30,000-45,000 FWHM resolution at m/z 200 with an AGC of 4 x 105 ions, injection time of 250 ms, HCD NCE of 15%, and a precursor isolation width of 1.6 Th.
  5. Data analysis of LC-MS/MS glycoproteomics data
    1. Confirm the enrichment of LC-MS/MS raw data with N-glycopeptides by checking that most HCD-MS/MS spectra contain glycan oxonium ions (e.g., m/z 138.0545, 204.0867 and 366.1396) and (for labeling strategies) feature baseline separation of the low mass TMT reporter ions, high fragmentation efficiency in the MS/MS spectra and narrow LC peak width, and high MS accuracy, resolution and signal-to-noise.
    2. Using a search engine (multiple are available; see below), search the MS/MS data for intact N-glycopeptides against a suitable protein/peptide database targeted at the sample being investigated (e.g., obtained from a de-N-glycoproteomics experiment) or against all reviewed UniProtKB human proteins. Tailor the search against peptide N-glycosylation by searching for peptides with sequon-localized Asn. Importantly, select a glycomics-informed N-glycan database, i.e., glycans identified from glycomics data in step 2, to restrict the search space to glycans known to exist in the sample(s).
      NOTE: Parallel searches using a broad predefined N-glycan database may be considered to rule out the existence of additional N-glycan compositions in the data.
    3. Tailor the remaining search variables according to the sample being studied, the specific sample handling procedures performed, and the employed MS settings and used instrumentation. Common search settings for the N-glycoproteomics data search include:
      Precursor and product mass tolerance: 10 ppm and 20 ppm
      Protease specificity: Fully specific and/or semi-specific (N-ragged)
      Permitted missed cleavages: 2
      Fixed modifications: Cys carbamidomethylation (+57.021 Da)
      Variable modifications: Met oxidation (+15.994 Da) (common 1)
      Permitted variable modifications per peptide: 1 or 2
      Glycan modifications: Glycomics-informed glycan database (rare 1)
      ​Protein decoy and contaminant database: Enabled
    4. Filter all searches to <1% false discovery rate (FDR) at the protein level and peptide level. Consider only N-glycopeptides identified with high confidence (e.g., PEP 2D scores < 0.001) and perform manual annotation/curation of the filtered output to reduce further the rate of incorrect N-glycopeptide identification as discussed36,40,41.
      NOTE: Glycopeptides with sialylated and multi-fucosylated N-glycans and glycopeptides carrying other variable modifications in addition to glycans (i.e., Met oxidation and Asn/Gln deamidation) are particularly prone to misinterpretation by the search engines41. Particular attention should be paid to validate such candidates prior to reporting.

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Results

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Representative results of the glycomics-guided glycoproteomics method applied to a FFPE tissue slide from a patient suffering from CRC in stage II are provided in this section.

To achieve sufficient protein starting material for the protocol, protein extracts from two slides (z stacks) were combined from the same FFPE tissue block following a published protocol and a TMT labeling approach was applied to increase the sensitivity of the glycoproteomics experiment (see optional steps below)

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Discussion

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Critical steps in the protocol
In this protocol paper, we have outlined step-by-step the glycomics-guided glycoproteomics method, which provides a comprehensive view of the glycoproteome's heterogeneity and dynamics in the complex TME.

A critical step in the protocol is to ensure complete proteolysis without over-digesting the protein mixture. Non-specific cleavages of proteins inherently increase the peptide search space, leading to longer search times and a higher ...

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Disclosures

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,

The authors declare no conflict of interest.

Acknowledgements

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THC is supported by an International Research Training Program Scholarship funded by the Australian Government. NB is supported by International Macquarie University Research Excellence Scholarships funded by Macquarie University. AC is supported by a Research Training Program scholarship funded by the Australian Government. RK was supported by the Cancer Institute of New South Wales (ECF181259). MTA is the recipient of an Australian Research Council Future Fellowship (FT210100455).

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Materials

List of materials used in this article
NameCompanyCatalog NumberComments
Chemicals
Ammonium acetate Sigma AldrichA1542Alternatives available
Ammonium bicarbonate, purity ≥99.0%Sigma AldrichA6141Alternatives available
Anhydrous acetonitrile (ACN), LC-MS gradeSigma Aldrich34851Alternatives available
Bovine fetuinSigma AldrichF3004Alternatives available
Dithiothreitol (DTT)Sigma AldrichD0632Alternatives available
EthanolSigma AldrichE7023Alternatives available
Formic acid, LC-MS gradeSigma Aldrich00940Alternatives available
Glacial acetic acidSigma AldrichA6283Alternatives available
Iodoacetamide (IAA)Sigma AldrichI1149Alternatives available
MethanolSigma AldrichM1775Alternatives available
Peptide-N-glycosidase F (PNGase F)PromegaV4831Elizabethkingia miricola PNGase F (10 U/μL) recombinantly expressed in Escherichia coli 
Polyvinylpyrrolidone 40 (PVP)Sigma AldrichPVP40Alternatives available
Potassium hydroxide (KOH)Sigma Aldrich484016Alternatives available
Sequencing-grade porcine trypsin PromegaV5113Alternatives available
Sodium borohydride (NaBH4)Sigma Aldrich213462Alternatives available
Triethylammonium bicarbonate (TEAB), LC-MS gradeSigma AldrichT7408Alternatives available
Trifluoroacetic acid (TFA), LC-MS gradeSigma AldrichT6508Alternatives available
Tools/Materials
C18 disksEmpore66883-U
C8 disksEmpore66882-U
Flat-bottom polypropylene 96-well plate Corning3364
Immobilon-PSQ polyvinylidene difluoride (PVDF) membraneMilliporeIPVH20200Pore size: 0.45 μm 
MicrocentrifugeEppendorf5452000069Alternatives available
Microcentrifuge adapters The Nest Group, Inc., MASS18VMiniSpin Column Collar, comes with Microspin Columns
Oligo R3 resinThermo1133903Particle size 30 μm
ParafilmParafilm MPM996
Protein LoBind tubes 1.5 mLEppendorf0030108450
Protein LoBind tubes 2.0 mLEppendorf0030108442
Safe-lock tubes 1.5 mLEppendorf0030120086
Safe-lock tubes 2.0 mLEppendorf0030120094
ShakerEppendorf5382000066Alternatives available
Single hole puncherSwingline74005
SpeedVac concentratorMartin ChristRVC 2-25 CDplusAlternatives available
Supelclean ENVI-Carb SPE resinSupelco57088Particle size 120-400 mesh. Resin can be manually extracted from cartridges, and can be stored long-term in 50% (v/v) methanol in MilliQ water
Syringe 5 mLSigma AldrichZ116866Alternatives available
Temperature-controlled incubatorThermolineE7.30Alternatives available
Ultrasonic bathUnisonicsFXPAlternatives available
ZIC-HILIC resinMerck150455Particle/pore size, 5 μm/200 Å. Resin can be manually extracted from LC column, and can be stored long-term in 50% (v/v) methanol in MilliQ water
ZipTip C18 solid-phase extraction (SPE) micro-columns MilliporeZTC18S096Alternatively, home-made C18-SPE micro-columns can replace commercial product
LC-MS/MS Analysis
1260 Infinity Capillary HPLC system AgilentAlternatives available
Analytical LC column pre-packed with ReproSil-Pur C18 AQ resinDr Maisch, Ammerbuch-Entringen, Germanyr13.aq.s2570Particle/pore size: 3 μm/120 Å, length/inner diameter: 25 cm/75 μm. Commercial C18 nano-LC columns also available/ 
Dionex UltiMate 3000 RSLCnano LC System ThermoAlternatives available
HyperCarb KAPPA PGC capillary LC column ThermoDiscontinuedParticle/pore size, 3 μm/250 Å; column length, 30 mm; inner diameter, 0.180 mm. Larger geometries of the HyperCarb PGC-LC columns, producing similar quality data are commercially available (e.g., Thermo #35003-031030). SupelTMCarb LC column from Merck with a particle/pore size, 2.7 μm/200 and with different column lengths and internal diameter are also available (e.g., Merck #59994-U). 
LCMS-grade acetonitrileLiChrosolv100029Alternatives available
Linear trap quadrupole (LTQ) Velos Pro ion trap mass spectrometer (glycomics) ThermoAlternatives available
Orbitrap Fusion Lumos Tribrid Mass Spectrometer (glycoproteomics)ThermoOther high-resolution MS systems with or without ETD (only required for O-glycoproteomics) are also available (e.g., Q-Exactive HF-X Hybrid Quadrupole-Orbitrap, Thermo, TIMS-ToF-MS, Bruker or similar high performance mass spectrometers from other vendors)
Total Recovery Clear Glass screw vials 0.9 mLThermoTHC11093563Alternatives available
Total Recovery Clear Glass screw vials matching capsThermoTHC09150869Alternatives available
Trap LC column packed in-house with ReproSil-Pur C18 AQ resin Dr Maisch, Ammerbuch-Entringen, Germanyr15.aq.s0202Particle/pore size: 5 μm/120 Å, length/inner diameter: 2 cm/100 μm. Commercial C18 trap LC columns also available. 
Software
ByonicProtein Metrics Incv2.6.46 or higherCommercial glycopeptide and PTM search engine, accepting LC-MS/MS raw data from most MS vendors
ByosProtein Metrics Incv3.9-7 or higherCommercial software for manual inspection of glycopeptide candidates and the automatic annotation of glycan MS/MS spectra
GlycoModExpasyOpen access software, assisting the annotation of glycomics data (https://web.expasy.org/glycomod/)
GlycoWorkBenchEUROCarbDBv2.1Open access software, assisting the annotation of glycomics data and the drawing of glycan cartoons
RawMeatVAST Scientificv2.1Open access software, extracting m/z of glycan precursor ions from raw spectral data
SkylineBrendan X. MacLeanv21.2 or higherOpen access software for relative quantitation of glycans
XcaliburThermov2.2 or higherFor browsing raw LC-MS/MS data

References

$$\rightleftharpoonup{xx}$$ $$\longleftharp{xx}$$, $$\longrightharp{xx}$$,
  1. Gagneux, P., Panin, V., Hennet, T., Aebi, M., Varki, A. Essentials of glycobiology. , Cold Spring Harbor. NY. 265-278 (2022).
  2. Tjondro, H. C., Loke, I., Chatterjee, S., Thaysen-Andersen, M. Human protein paucimannosylation: Cues from the eukaryotic kingdoms. Biol Rev Camb Philos Soc. 94 (6), 2068-2100 (2019).
  3. West, C. M., Malzl, D., Hykollari, A., Wilson, I. B. H. Glycomics, glycoproteomics, and glycogenomics: An inter-taxa evolutionary perspective. Mol Cell Proteomics. 20, 100024(2021).
  4. Varki, A. Biological roles of glycans. Glycobiology. 27 (1), 3-49 (2016).
  5. Reily, C., Stewart, T. J., Renfrow, M. B., Novak, J. Glycosylation in health and disease. Nat Rev Nephrol. 15 (6), 346-366 (2019).
  6. Leite-Gomes, E., et al. Bringing to light the risk of colorectal cancer in inflammatory bowel disease: Mucosal glycosylation as a key player. Inflamm Bowel Dis. 28 (6), 947-962 (2022).
  7. He, K., et al. Decoding the glycoproteome: A new frontier for biomarker discovery in cancer. J Hematol Oncol. 17 (1), 12(2024).
  8. Mereiter, S., Balmana, M., Campos, D., Gomes, J., Reis, C. A. Glycosylation in the era of cancer-targeted therapy: Where are we heading. Cancer Cell. 36 (1), 6-16 (2019).
  9. Seeberger, P. H., et al. What comes next in glycobiology. Cell. 187 (11), 2628-2632 (2024).
  10. Abrahams, J. L., et al. Recent advances in glycoinformatic platforms for glycomics and glycoproteomics. Curr Opin Struct Biol. 62, 56-69 (2020).
  11. Thaysen-Andersen, M., Packer, N. H., Schulz, B. L. Maturing glycoproteomics technologies provide unique structural insights into the N-glycoproteome and its regulation in health and disease. Mol Cell Prot. 15 (6), 1773-1790 (2016).
  12. Gaunitz, S., Nagy, G., Pohl, N. L., Novotny, M. V. Recent advances in the analysis of complex glycoproteins. Anal Chem. 89 (1), 389-413 (2017).
  13. Chernykh, A., Kawahara, R., Thaysen-Andersen, M. Towards structure-focused glycoproteomics. Biochem Soc Trans. 49 (1), 161-186 (2021).
  14. Hu, H., Khatri, K., Zaia, J. Algorithms and design strategies towards automated glycoproteomics analysis. Mass Spectrom Rev. 36 (4), 475-498 (2017).
  15. Bagdonaite, I., et al. Glycoproteomics. Nat Rev Meth Primers. 2 (1), 48(2022).
  16. Peng, W., et al. MS-based glycomics and glycoproteomics methods enabling isomeric characterization. Mass Spectrom Rev. 42 (2), 577-616 (2023).
  17. Aoki-Kinoshita, K. F., et al. Essentials of glycobiology. , Cold Spring Harbor Laboratory Press. NY. 705-718 (2022).
  18. Thomas, D. R., Scott, N. E. Glycoproteomics: Growing up fast. Curr Opin Struct Biol. 68, 18-25 (2021).
  19. Thaysen-Andersen, M., Kolarich, D., Packer, N. H. Glycomics & glycoproteomics: From analytics to function. Mol Omics. 17 (1), 8-10 (2021).
  20. Trbojevic-Akmacic, I., et al. High-throughput glycomic methods. Chem Rev. 122 (20), 15865-15913 (2022).
  21. Oliveira, T., Thaysen-Andersen, M., Packer, N. H., Kolarich, D. The hitchhiker's guide to glycoproteomics. Biochem Soc Trans. 49 (4), 1643-1662 (2021).
  22. Rudd, P. M., et al. Essentials of glycobiology. , Cold Spring Harbor. NY. 689-704 (2022).
  23. Madunić, K., et al. Colorectal cancer cell lines show striking diversity of their O-glycome reflecting the cellular differentiation phenotype. Cell Mol Life Sci. 78 (1), 337-350 (2021).
  24. Parker, R., et al. Mapping the SARS-CoV-2 spike glycoprotein-derived peptidome presented by HLA class II on dendritic cells. Cell Rep. 35 (8), 109179(2021).
  25. Zhao, P., et al. Virus-receptor interactions of glycosylated SARS-CoV-2 spike and human ACE2 receptor. Cell Host Microbe. 28 (4), 586-601.e6 (2020).
  26. Chatterjee, S., et al. Serum N-glycomics stratifies bacteremic patients infected with different pathogens. J Clin Med. 10 (3), 516(2021).
  27. Osimanjiang, W., et al. Analysis of N- and O-linked glycosylation: Differential glycosylation after rat spinal cord injury. J Neurotrauma. 37 (18), 1954-1962 (2020).
  28. Chatterjee, S., et al. Trends in oligomannosylation and α1,2-mannosidase expression in human cancers. Oncotarget. 12 (21), 2188-2205 (2021).
  29. Chang, D., Klein, J. A., Nalehua, M. R., Hackett, W. E., Zaia, J. Data-independent acquisition mass spectrometry for site-specific glycoproteomics characterization of SARS-CoV-2 spike protein. Anal Bioanal Chem. 413 (29), 7305-7318 (2021).
  30. Pan, J., et al. Glycoproteomics-based signatures for tumor subtyping and clinical outcome prediction of high-grade serous ovarian cancer. Nat Commun. 11 (1), 6139(2020).
  31. Venkatakrishnan, V., et al. Glycan analysis of human neutrophil granules implicates a maturation-dependent glycosylation machinery. J Biol Chem. 295 (36), 12648-12660 (2020).
  32. Sethi, M. K., et al. In-depth matrisome and glycoproteomic analysis of human brain glioblastoma versus control tissue. Mol Cell Proteom. 21 (4), 100216(2022).
  33. Parker, B. L., et al. Site-specific glycan-peptide analysis for determination of N-glycoproteome heterogeneity. J Proteome Res. 12 (12), 5791-5800 (2013).
  34. Jensen, P. H., Karlsson, N. G., Kolarich, D., Packer, N. H. Structural analysis of N- and O-glycans released from glycoproteins. Nat Protoc. 7 (7), 1299-1310 (2012).
  35. Hinneburg, H., et al. Post-column make-up flow (pcmf) enhances the performance of capillary-flow PGC-LC-MS/MS-based glycomics. Anal Chem. 91 (7), 4559-4567 (2019).
  36. Chau, T. H., et al. Glycomics-assisted glycoproteomics enables deep and unbiased N-glycoproteome profiling of complex biological specimens. Meth Mol Biol. 2628, 235-263 (2023).
  37. Madunic, K., Wagt, S., Zhang, T., Wuhrer, M., Lageveen-Kammeijer, G. S. M. Dopant-enriched nitrogen gas for enhanced electrospray ionization of released glycans in negative ion mode. Anal Chem. 93 (18), 6919-6923 (2021).
  38. Zhang, T., Wang, W., Wuhrer, M., De Haan, N. Comprehensive O-glycan analysis by porous graphitized carbon nanoliquid chromatography-mass spectrometry. Anal Chem. 96 (22), 8942-8948 (2024).
  39. Wu, S. W., Pu, T. H., Viner, R., Khoo, K. H. Novel LC-MS2 product dependent parallel data acquisition function and data analysis workflow for sequencing and identification of intact glycopeptides. Anal Chem. 86 (2), 5478-5486 (2014).
  40. Kawahara, R., et al. The complexity and dynamics of the tissue glycoproteome associated with prostate cancer progression. Mol Cell Proteom. 20, 100026(2021).
  41. Chau, T. H., Chernykh, A., Kawahara, R., Thaysen-Andersen, M. Critical considerations in N-glycoproteomics. Curr Opin Chem Biol. 73, 102272(2023).
  42. Carnielli, C. M., et al. Comprehensive glycoprofiling of oral tumors associates N-glycosylation with lymph node metastasis and patient survival. Mol Cell Proteom. 22 (7), 100586(2023).
  43. Sethi, M. K., et al. In-depth N-glycome profiling of paired colorectal cancer and non-tumorigenic tissues reveals cancer-, stage- and EGFR-specific protein N-glycosylation. Glycobiology. 25 (10), 1064-1078 (2015).
  44. Magdeldin, S., Yamamoto, T. Toward deciphering proteomes of formalin-fixed paraffin-embedded (FFPE) tissues. Proteomics. 12 (7), 1045-1058 (2012).
  45. Ostasiewicz, P., Zielinska, D. F., Mann, M., Wiśniewski, J. R. Proteome, phosphoproteome, and N-glycoproteome are quantitatively preserved in formalin-fixed paraffin-embedded tissue and analyzable by high-resolution mass spectrometry. J Proteome Res. 9 (7), 3688-3700 (2010).
  46. Tian, Y., Gurley, K., Meany, D. L., Kemp, C. J., Zhang, H. N-linked glycoproteomic analysis of formalin-fixed and paraffin-embedded tissues. J Proteome Res. 8 (4), 1657-1662 (2009).
  47. Cozzolino, F., et al. New label-free methods for protein relative quantification applied to the investigation of an animal model of huntington disease. PLoS One. 15 (9), e0238037(2020).
  48. Sethi, M. K., et al. Quantitative proteomic analysis of paired colorectal cancer and non-tumorigenic tissues reveals signature proteins and perturbed pathways involved in CRC progression and metastasis. J Proteomics. 126, 54-67 (2015).
  49. Rosati, D., et al. Differential gene expression analysis pipelines and bioinformatic tools for the identification of specific biomarkers: A review. Comput Struct Biotechnol J. 23, 1154-1168 (2024).
  50. Niu, B., et al. Nonspecific cleavages arising from reconstitution of trypsin under mildly acidic conditions. PLoS One. 15 (7), e0236740(2020).
  51. Delafield, D. G., Li, L. Recent advances in analytical approaches for glycan and glycopeptide quantitation. Mol Cell Proteom. 20, 100054(2021).
  52. Viner, R. I., Snovida, S., Bodnar, E., Perreault, H., Saba, J. A novel workflow for glycopeptide analysis using cellulose-based separation cartridges, TMT-labeling and LTQ Orbitrap ETD. J Biomol Tech. 21 (3 Suppl), S25(2010).
  53. Kawahara, R., et al. Community evaluation of glycoproteomics informatics solutions reveals high-performance search strategies for serum glycopeptide analysis. Nat Meth. 18 (11), 1304-1316 (2021).
  54. Polasky, D. A., Nesvizhskii, A. I. Recent advances in computational algorithms and software for large-scale glycoproteomics. Curr Opin Chem Biol. 72, 102238(2023).
  55. Polasky, D. A., Yu, F., Teo, G. C., Nesvizhskii, A. I. Fast and comprehensive N- and O-glycoproteomics analysis with MSFragger-Glyco. Nat Meth. 17 (11), 1125-1132 (2020).
  56. Goodson, H., et al. Α-mannosylated HLA-II glycopeptide antigens dominate the immunopeptidome of immortalised cells and tumour tissues. Glycobiology. 34 (11), cwae057(2024).
  57. Shajahan, A., Heiss, C., Ishihara, M., Azadi, P. Glycomic and glycoproteomic analysis of glycoproteins-a tutorial. Anal Bioanal Chem. 409 (19), 4483-4505 (2017).
  58. Rosenbalm, K. E., Tiemeyer, M., Wells, L., Aoki, K., Zhao, P. Glycomics-informed glycoproteomic analysis of site-specific glycosylation for SARS-CoV-2 spike protein. STAR Prot. 1 (3), 100214-100214 (2020).
  59. Zhao, P., et al. Virus-receptor interactions of glycosylated SARS-CoV-2 spike and human ACE2 receptor. Cell Host Microbe. 28 (4), 586-601.e6 (2020).
  60. Chandler, K. B., et al. Multi-isotype glycoproteomic characterization of serum antibody heavy chains reveals isotype- and subclass-specific N-glycosylation profiles. Mol Cell Proteomics. 18 (4), 686-703 (2019).
  61. Haab, B. B., Klamer, Z. Advances in tools to determine the glycan-binding specificities of lectins and antibodies. Mol Cell Proteom. 19 (2), 224-232 (2020).
  62. Zhang, L., Luo, S., Zhang, B. The use of lectin microarray for assessing glycosylation of therapeutic proteins. MAbs. 8 (3), 524-535 (2016).
  63. Goumenou, A., Delaunay, N., Pichon, V. Recent advances in lectin-based affinity sorbents for protein glycosylation studies. Front Mol Biosci. 8, 746822(2021).
  64. Parker, B. L., et al. Terminal galactosylation and sialylation switching on membrane glycoproteins upon TNF-alpha-induced insulin resistance in adipocytes. Mol Cell Proteomics. 15 (1), 141-153 (2016).
  65. Blazev, R., et al. Integrated glycoproteomics identifies a role of N-glycosylation and galectin-1 on myogenesis and muscle development. Mol Cell Proteomics. 20, 100030(2021).
  66. Thaysen-Andersen, M., et al. Human neutrophils secrete bioactive paucimannosidic proteins from azurophilic granules into pathogen-infected sputum. J Biol Chem. 290 (14), 8789-8802 (2015).
  67. Loke, I., Ostergaard, O., Heegaard, N. H. H., Packer, N. H., Thaysen-Andersen, M. Paucimannose-rich N-glycosylation of spatiotemporally regulated human neutrophil elastase modulates its immune functions. Mol Cell Proteom. 16 (8), 1507-1527 (2017).
  68. Loke, I., Packer, N. H., Thaysen-Andersen, M. Complementary LC-MS/MS-based N-glycan, N-glycopeptide, and intact N-glycoprotein profiling reveals unconventional ASN71-glycosylation of human neutrophil cathepsin g. Biomolecules. 5 (3), 1832-1854 (2015).
  69. Tjondro, H. C., et al. Hyper-truncated Asn355- and Asn391-glycans modulate the activity of neutrophil granule myeloperoxidase. J Biol Chem. 296, 100144(2021).
  70. Kawahara, R., et al. Glycoproteome remodeling and organelle-specific N-glycosylation accompany neutrophil granulopoiesis. Proc Natl Acad Sci U S A. 120 (36), e2303867120(2023).

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Glycomics Guided GlycoproteomicsTumor MicroenvironmentMass SpectrometryGlycan StructuresN Glycoproteome ProfilingPorous Graphitized CarbonLC MS MSGlycopeptide EnrichmentSite Specific GlycosylationColorectal Cancer Tissue

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