In clinical chemistry, disease-specific biomarkers are highly important for diagnosis. Discovery of these biomarkers often involves large sample cohorts, which have to be analyzed within an as short as possible amount of time. To complete this analysis in a reasonable amount of time, it is essential to use a high-throughput analyzing method. For glycosylation analysis of these cohort studies, we use a high-throughput automated system for sialic acid derivatization, HILIC purification and MALDI-target spotting. The method requires minimal amounts of manual intervention, which is beneficial for the repeatability of the experiments. Due to time-efficient procedure scheduling, the robotized system can processes up to eight hundred samples per day, with subsequent measurement on a MALDI-MS system. The first set of 96 samples has a throughput time of approximately 2.5 hours with an additional hour for every extra 96 samples.1

(LC–)MS data processing

Extracting the relevant information from large and complex data sets produced by high throughput methods in combination with mass spectrometric readout is a formidable challenge. Currently, data processing of glycomics and glycoproteomics data requires much more hands-on work than in more mature fields, for example proteomics. We have addressed some of these bottlenecks by developing software capable of automated data extraction from complex mass spectrometric glycan and glycopeptide analyses.2-5 Our efforts are aimed at relative quantitation of glycoforms from targeted analysis approaches. Our latest software packages for MALDI-MS and LC–MS are called MassyTools4 and LaCyTools,5 respectively. By utilizing the full complexity of MS data, these bring out the excellent robustness and accuracy of our instrumental methods. Reported quality criteria enable the scientist to rapidly assess the reliability of their results. This automated data processing is instrumental in our ability to efficiently study large clinical cohorts.

Standardized workflows and statistics

With the development of automated workflows for MALDI-MS analysis of glycosylation, the need arises for automated processing of the data.1 Examples of advances in data integration and measurement quality control can be found in MassyTools and LaCyTools.4,5 Hereafter, standardization is required for the further processing of the data, as well as the statistical analysis thereof. The compositional glycan information acquired by MS puts us in the strong position to calculate ratios between the glycans, reducing the measurement error and providing additional information of biological relevance1. These derived traits can range from the simple summing of e.g. high-mannose type glycans within a spectrum, to complex ratio calculations such as the sialic acids per galactose within the subset of fucosylated tetraantennary species.

The scripted usage of these derived glycosylation traits, as well as scripted and standardized statistical analysis, has allowed us to reveal the complexity of glycosylation changes that occur throughout early life and pregnancy4,5.

  1. Bladergroen, M. R. et al. Automation of High-Throughput Mass Spectrometry-Based Plasma N‑Glycome Analysis with Linkage-Specific Sialic Acid Esterification. J. Proteome Res. 14, 4080-4086 (2015)
  2. Reiding, K. R., Blank, D., Kuijper, D. M., Deelder, A. M. & Wuhrer, M. High-throughput profiling of protein N-glycosylation by MALDI-TOF-MS employing linkage-specific sialic acid esterification. Anal. Chem. 86, 5784-5793 (2014).
  3. Falck, D. et al. Glycoforms of Immunoglobulin G Based Biopharmaceuticals Are Differentially Cleaved by Trypsin Due to the Glycoform Influence on Higher-Order Structure. J. Proteome Res. 14, 4019-4028 (2015).
  4. Jansen, B. C. et al. MassyTools: A High-Throughput Targeted Data Processing Tool for Relative Quantitation and Quality Control Developed for Glycomic and Glycoproteomic MALDI-MS. J. Proteome Res. 14, 5088-5098 (2015).
  5. Jansen, B. C. et al. LaCyTools: A Targeted Liquid Chromatography-Mass Spectrometry Data Processing Package for Relative Quantitation of Glycopeptides. J. Proteome Res. 15, 2198-2210 (2016).
  6. de Haan, N., Reiding, K. R., Driessen, G., van der Burg, M. & Wuhrer, M. Changes in Healthy Human IgG Fc-Glycosylation after Birth and during Early Childhood. J. Proteome Res. 15, 1853-1861 (2016).
  7. Jansen, B. C. et al. Pregnancy-associated serum N-glycome changes studied by high-throughput MALDI-TOF-MS. Sci. Rep. 6, 23296 (2016).