Changes to serum sample tube and processing methodology does not cause Intra-Individual [corrected] variation in automated whole serum N-glycan profiling in health and disease

Abstract

Introduction: Serum N-glycans have been identified as putative biomarkers for numerous diseases. The impact of different serum sample tubes and processing methods on N-glycan analysis has received relatively little attention. This study aimed to determine the effect of different sample tubes and processing methods on the whole serum N-glycan profile in both health and disease. A secondary objective was to describe a robot automated N-glycan release, labeling and cleanup process for use in a biomarker discovery system.

Methods: 25 patients with active and quiescent inflammatory bowel disease and controls had three different serum sample tubes taken at the same draw. Two different processing methods were used for three types of tube (with and without gel-separation medium). Samples were randomised and processed in a blinded fashion. Whole serum N-glycan release, 2-aminobenzamide labeling and cleanup was automated using a Hamilton Microlab STARlet Liquid Handling robot. Samples were analysed using a hydrophilic interaction liquid chromatography/ethylene bridged hybrid(BEH) column on an ultra-high performance liquid chromatography instrument. Data were analysed quantitatively by pairwise correlation and hierarchical clustering using the area under each chromatogram peak. Qualitatively, a blinded assessor attempted to match chromatograms to each individual.

Results: There was small intra-individual variation in serum N-glycan profiles from samples collected using different sample processing methods. Intra-individual correlation coefficients were between 0.99 and 1. Unsupervised hierarchical clustering and principal coordinate analyses accurately matched samples from the same individual. Qualitative analysis demonstrated good chromatogram overlay and a blinded assessor was able to accurately match individuals based on chromatogram profile, regardless of disease status.

Conclusions: The three different serum sample tubes processed using the described methods cause minimal inter-individual variation in serum whole N-glycan profile when processed using an automated workstream. This has important implications for N-glycan biomarker discovery studies using different serum processing standard operating procedures.

Fig 1. Biomarker discovery system workflow.

(SPE = solid phase extraction, UHPLC = Ultra high performance liquid chromatography)

Fig 2. Fluorescence chromatogram showing a 2-aminobenzamide labeled glucose homopolymer (GHP) run on a UHPLC HILIC column.

Fig 2A–2C and 2D–2F are derived from different patients respectively. Peaks are labeled arbitrarily in order from 1 to 42, with glucose unit values according to Guile et al [22] Peaks are colored alternately to aid identification.

Fig 3. Clustering correlation complete linkage a) Neutral glycans b) Total glycans.

Samples from the same individual are labeled using the same letter.

Fig 4. Principal coordinate analysis plot using classical multidimensional scaling of the (1−|Pearson’s r|) distance matrix for a) Neutral serum N-glycans b) Total serum N-glycans (CD: Crohn’s disease; UC: Ulcerative colitis; IBD: Inflammatory bowel disease).