Assessment of monoclonal antibody glycosylation: a comparative study using HRMS, NMR, and HILIC-FLD.

Monoclonal antibodies (mAbs) represent the largest class of therapeutic protein drug products. mAb glycosylation produces a heterogeneous, analytically challenging distribution of glycoforms that typically should be adequately characterized because glycosylation-based product quality attributes (PQAs) can impact product quality, immunogenicity, and efficacy. In this study, two products were compared using a panel of analytical methods. Two high-resolution mass spectrometry (HRMS) workflows were used to analyze N-glycans, while nuclear magnetic resonance (NMR) was used to generate monosaccharide fingerprints. These state-of-the-art techniques were compared to conventional analysis using hydrophilic interaction chromatography (HILIC) coupled with fluorescence detection (FLD). The advantages and disadvantages of each method are discussed along with a comparison of the identified glycan distributions. The results demonstrated agreement across all methods for major glycoforms, demonstrating how confidence in glycan characterization is increased by combining orthogonal analytical methodologies. The full panel of methods used represents a diverse toolbox that can be selected from based on the needs for a specific product or analysis.

Quantitative clinical glycomics strategies: A guide for selecting the best analysis approach.

Glycans introduce complexity to the proteins to which they are attached. These modifications vary during the progression of many diseases; thus, they serve as potential biomarkers for disease diagnosis and prognosis. The immense structural diversity of glycans makes glycosylation analysis and quantitation difficult. Fortunately, recent advances in analytical techniques provide the opportunity to quantify even low-abundant glycopeptides and glycans derived from complex biological mixtures, allowing for the identification of glycosylation differences between healthy samples and those derived from disease states. Understanding the strengths and weaknesses of different quantitative glycomics analysis methods is important for selecting the best strategy to analyze glycosylation changes in any given set of clinical samples. To provide guidance towards selecting the proper approach, we discuss four widely used quantitative glycomics analysis platforms, including fluorescence-based analysis of released N-linked glycans and three different varieties of MS-based analysis: liquid chromatography (LC)-mass spectrometry (MS) analysis of glycopeptides, matrix-assisted laser desorption ionization-time of flight MS, and LC-ESI-MS analysis of released N-linked glycans. These methods' strengths and weaknesses are compared, particularly associated with the figures of merit that are important for clinical biomarker studies, including: the initial sample requirements, the methods' throughput, sample preparation time, the number of species identified, the methods' utility for isomer separation and structural characterization, method-related challenges associated with quantitation, repeatability, the expertise required, and the cost for each analysis. This review, therefore, provides unique guidance to researchers who endeavor to undertake a clinical glycomics analysis by offering insights on the available analysis technologies.

The local-balanced model for improved machine learning outcomes on mass spectrometry data sets and other instrumental data.

One unifying challenge when classifying biological samples with mass spectrometry data is overcoming the obstacle of sample-to-sample variability so that differences between groups, such as between a healthy set and a disease set, can be identified. Similarly, when the same sample is re-analyzed under identical conditions, instrument signals can fluctuate by more than 10%. This signal inconsistency imposes difficulties in identifying subtle differences across a set of samples, and it weakens the mass spectrometrist's ability to effectively leverage data in domains as diverse as proteomics, metabolomics, glycomics, and imaging. We selected challenging data sets in the fields of glycomics, mass spectrometry imaging, and bacterial typing to study the problem of within-group signal variability and adapted a 30-year-old statistical approach to address the problem. The solution, "local-balanced model," relies on using balanced subsets of training data to classify test samples. This analysis strategy was assessed on ESI-MS data of IgG-based glycopeptides and MALDI-MS imaging data of endogenous lipids, and MALDI-MS data of bacterial proteins. Two preliminary examples on non-mass spectrometry data sets are also included to show the potential generality of the method outside the field of MS analysis. We demonstrate that this approach is superior to simple normalization methods, generalizable to multiple mass spectrometry domains, and potentially appropriate in fields as diverse as physics and satellite imaging. In some cases, improvements in classification can be dramatic, with accuracy escalating from 60% with normalization alone to over 90% with the additional development described herein.

The Aristotle Classifier: Using the Whole Glycomic Profile To Indicate a Disease State.

"The totality is not, as it were, a mere heap, but the whole is something besides the parts."-Aristotle. We built a classifier that uses the totality of the glycomic profile, not restricted to a few glycoforms, to differentiate samples from two different sources. This approach, which relies on using thousands of features, is a radical departure from current strategies, where most of the glycomic profile is ignored in favor of selecting a few features, or even a single feature, meant to capture the differences in sample types. The classifier can be used to differentiate the source of the material; applicable sources may be different species of animals, different protein production methods, or, most importantly, different biological states (disease vs healthy). The classifier can be used on glycomic data in any form, including derivatized monosaccharides, intact glycans, or glycopeptides. It takes advantage of the fact that changing the source material can cause a change in the glycomic profile in many subtle ways: some glycoforms can be upregulated, some downregulated, some may appear unchanged, yet their proportion-with respect to other forms present-can be altered to a detectable degree. By classifying samples using the entirety of their glycan abundances, along with the glycans' relative proportions to each other, the "Aristotle Classifier" is more effective at capturing the underlying trends than standard classification procedures used in glycomics, including PCA (principal components analysis). It also outperforms workflows where a single, representative glycomic-based biomarker is used to classify samples. We describe the Aristotle Classifier and provide several examples of its utility for biomarker studies and other classification problems using glycomic data from several sources.

Two New Tools for Glycopeptide Analysis Researchers: A Glycopeptide Decoy Generator and a Large Data Set of Assigned CID Spectra of Glycopeptides.

The glycopeptide analysis field is tightly constrained by a lack of effective tools that translate mass spectrometry data into meaningful chemical information, and perhaps the most challenging aspect of building effective glycopeptide analysis software is designing an accurate scoring algorithm for MS/MS data. We provide the glycoproteomics community with two tools to address this challenge. The first tool, a curated set of 100 expert-assigned CID spectra of glycopeptides, contains a diverse set of spectra from a variety of glycan types; the second tool, Glycopeptide Decoy Generator, is a new software application that generates glycopeptide decoys de novo. We developed these tools so that emerging methods of assigning glycopeptides' CID spectra could be rigorously tested. Software developers or those interested in developing skills in expert (manual) analysis can use these tools to facilitate their work. We demonstrate the tools' utility in assessing the quality of one particular glycopeptide software package, GlycoPep Grader, which assigns glycopeptides to CID spectra. We first acquired the set of 100 expert assigned CID spectra; then, we used the Decoy Generator (described herein) to generate 20 decoys per target glycopeptide. The assigned spectra and decoys were used to test the accuracy of GlycoPep Grader's scoring algorithm; new strengths and weaknesses were identified in the algorithm using this approach. Both newly developed tools are freely available. The software can be downloaded at http://glycopro.chem.ku.edu/GPJ.jar.

Clinically Viable Assay for Monitoring Uromodulin Glycosylation.

Uromodulin, also known as the Tamm-Horsfall protein or THP, is the most abundant protein excreted in human urine. It is associated with the progression of kidney diseases; therefore, changes in the glycosylation profile of this protein could serve as a potential biomarker for kidney health. The typical glycomics analysis approaches used to quantify uromodulin glycosylation involve time-consuming and tedious glycoprotein isolation and labeling steps, which limit their utility in clinical glycomics assays, where sample throughput is important. Herein, we introduce a radically simplified sample preparation workflow, with direct ESI-MS analysis, enabling the quantification of N-linked glycans that originate from uromodulin. The method omits any glycan labeling steps but includes steps to reduce the salt content of the samples, thereby minimizing ion suppression. The method is effective for quantifying subtle glycosylation differences of uromodulin samples derived from different biological states. As a proof of concept, glycosylation from samples that differ by pregnancy status were shown to be differentiable.