Facilitating released N-glycan identification using LC-MS and an extensive glycan library


Featuring SCIEX OS software

Zhiliang Xiao,1 Ji Luo,1 Lihai Guo,1 Zhengwei Chen2
1SCIEX, China; 2SCIEX, USA

Introduction

In this technical note, an N-glycan library is introduced as a useful tool for fast identification of the released N-glycans from protein therapeutics. This library is fully integrated into SCIEX OS software for routine automated analysis. The three most commonly used labeling reagents—2-aminobenzamide (2-AB), procainamide (ProA) and RapiFluor (RF)—are incorporated to meet the diverse needs of biopharma-focused LC-MS analysis.

Biotherapeutics have been the fastest-growing modality in the therapeutic segment over the past decade. They have played an essential role in treating various diseases such as tumors, metabolic disorders and autoimmune diseases. Glycosylation is a common post-translational modification (PTM) in biotherapeutics: more than 70% of protein therapeutics are glycosylated proteins. Glycosylation has been shown to have a profound impact on the safety and efficacy of protein therapeutics and thus is usually considered to be a critical quality attribute (CQA). The high heterogeneity of N-glycosylation in protein therapeutics expressed in Chinese hamster ovary (CHO) cell lines brings significant challenges for the released glycan analysis. However, there are inherent rules for the N-glycosylation biosynthesis pathway in CHO cell lines. That is, every N-glycan starts with the formation of 2 GlcNAc and 3 Man at the core and is later modified by adding other sugar units to generate high-mannose, complex and hybrid N-glycans. The recognition of these conserved biosynthesis pathways could be used to predict and construct an N-glycan library.

An N-glycan library of 236 N-glycans was created based on the N-glycan biosynthesis pathway in CHO cell lines. Released glycan analysis of rituximab, a chimeric monoclonal antibody (mAb), is demonstrated using the TripleTOF 5600+ system from SCIEX. A single software platform (SCIEX OS software) was used, which offers a robust data processing solution for released glycan analysis, covering extracted ion chromatogram (XIC) generation, glycan identification, data filtering and report generation. This library can be applied to any SCIEX accurate mass system due to their similarity in fragmentation. The workflow is also applicable to other types of protein therapeutics.

Key features of the N-glycan library in SCIEX  OS software

  • A comprehensive picture of the N-glycans expressed in CHO cell lines is covered through an N-glycan library of 236 N-glycan structures corresponding to a total of 177 compositions.
    Fast processing and comprehensive identification of N-glycosylations of protein therapeutics can be achieved easily by applying the library in SCIEX OS software.
  • Flexibility of the sample preparation is encompassed by incorporating all three of the most commonly used N-glycan labeling reagents (2-AB, ProA and RF) into the released N-glycan library presented.
  • Versatility of the data acquisition platform is enabled by the compatibility of SCIEX OS software with processing data from all SCIEX accurate mass systems. 

 

Methods

Sample preparation: The sample preparation was conducted according to the RF kit protocol. Briefly, 15 μg of the rituximab mAb were denatured at 95°C after being dissolved in RapiGest SF buffer. N-glycans were released by PNGase F and then labeled by RF The labeled N-glycans were purified using HILIC-SPE before being injected for LC-MS analysis.

LC-MS conditions: The processed samples were subject to LC-MS/MS analysis using the ExionLC AD system coupled to a TripleTOF 5600+ system in positive ionization mode. LC separation was achieved on a C18 column (ACQUITY UPLC Glycan BEH Amide, 1.7 μm, 2.1 x 150 mm) with 50 mM ammonium formate at pH 4 as mobile phase A and acetonitrile as mobile phase B at a flow rate of 0.2–0.4 mL/min. The column was kept at 60°C, and 10 μL of sample were injected. The experimental details are summarized in Table 1 and Table 2.

Table 1. MS parameters.

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Table 2. Gradient used for LC separation.

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Data processing: The constructed N-glycan library was implemented into the SCIEX OS software (Figure 2). The embedded “Quantitation and targeted identification” workflow was used to allow relative quantification and targeted identification of various N-glycan species. In the workflow, the accurate mass and chemical formula of each N-glycan from the library at different charge states (+2, +3) were entered in the “Components” section in the software. Generic integration parameters were set for peak integration, which can be adjusted in the results file if needed. The processing method was then used to reduce the raw data files generated from the released N-glycan samples.

Results and discussion

Although the structures of N-glycan are diverse with different compositions and branches, the biosynthesis pathway follows a specific conservative pattern. The common core Man3GlcNAc2 is formed first, and complex N-glycans are then generated by the addition of further sugar units to this core structure. High-mannose N-glycans are created by the addition of up to six mannoses. In complex N-glycans, the “antennae” initiated by GlcNAc extend the core and can be further extended by galactose, sialic acids, etc. In hybrid N-glycans, mannoses extend one arm of the core, and one or two GlcNAcs extend the other arm.

Based on the structural nature of N-glycans, described previously, the N-glycan library was constructed with a total of 236 N-glycan structures. To be consistent with the N-glycan naming system used in the glycobiology community, the Oxford naming nomenclature was adopted for the name annotation, while the Consortium for Functional Glycomics (CFG) nomenclature was used for the structural annotation. To provide a comprehensive N-glycan library that accounts for different labeling reagents, the three most commonly used N-glycan labeling reagents (2-AB, ProA and RF) were incorporated into the library with their chemical formula and accurate mass information (Figure 1).

Figure 1. Overview of a part of the N-glycan library. The N-glycan library consists of 236 entries. Oxford notation was used to name the N-glycans, while CFG notation was used for the structure representation. Chemical formulas and monoisotopic masses from both native N-glycans and labeled Nglycans (2-AB, ProA and RF) were included in the library.

The constructed library was incorporated into the processing method in SCIEX OS software, which was used for processing raw data in batches and to generate results files. Subsequently, a user can validate the N-glycan identifications based on the spectra (Figure 2). Flexible filtering options for the results table— based on user-defined criteria such as mass accuracy, isotopic distribution, retention time and integration area—greatly facilitate the confirmation process within SCIEX OS software (area A in Figure 2). During review of the XICs panel, integration parameters can be adjusted to ensure reliable integration accuracy and accurate relative quantification of each N-glycan species for the chosen LC settings. For each N-glycan identification, the MS/MS spectra information was displayed alongside the MS spectra and retention times, which allows results table as well as the XICs, MS spectra and MS/MS further structural elucidation by looking into signature ions for isomers (areas B, C and D in Figure 2).

By using this approach for the released N-glycan analysis of rituximab, a total of 16 N-glycans were confidently identified at various levels, as shown in Figure 3. The report generated in SCIEX OS software provided information such as glycan composition, accurate mass, integration area and chemical formula (Figure 4). This report can also be customized for different characterization requirements.

Figure 2. Data processing in SCIEX OS software. Based on the accurate mass match, the N-glycan composition was assigned to each peak (A). The assigned N-glycans were further validated by checking the XIC integration area and retention time (B) as well as the isotopic distribution of the precursor (C) and the derived MS/MS spectrum (D).

Figure 3. XICs of 16 N-glycans identified in the rituximab mAb sample. XICs were automatically generated according to the m/z information from the constructed N-glycan library.

Figure 4. Report summarizing 16 identified N-glycans in the rituximab mAb sample. The table was generated through the report function of SCIEX OS software.

Conclusions

  • The established N-glycan library enables scientists to perform fast, comprehensive and reliable glycan analyses for protein therapeutics.
  • Confidence in the workflow is achieved through direct access of all relevant information, such as XIC and retention time, isotopic patterns of the precursors and the related MS/MS spectrum within SCIEX OS software.
  • Robust data processing for released glycan analysis— covering XIC extraction, glycan identification, data filtering and report generation—is covered in a single software platform, SCIEX OS software.
  • Accurate N-glycan identification and relative quantification is achieved routinely with the data generated by SCIEX accurate mass instrumentation. 

 

References

  1. Characterization of 2-AB labelled released N-linked glycans using the X500B QTOF system. SCIEX Technical Note, RUO-MKT-02-9202-A.
  2. Characterization of 2-AB labeled released N-linked glycans using SCIEX TripleTOF 5600+ LC-MS/MS system. SCIEX Technical Note, RUO-MKT-02-10500-A.
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