LC-MS/MS Isomeric Profiling of N-Glycans Derived from Low-Abundant Serum Glycoproteins in Mild Cognitive Impairment Patients

Abstract

Mild cognitive impairment (MCI) is an early stage of memory loss that affects cognitive abilities, such as language or virtual/spatial comprehension. This cognitive decline is mostly observed with the aging of individuals. Recently, MCI has been considered as a prodromal phase of Alzheimer's disease (AD), with a 10-15% conversion rate. However, the existing diagnostic methods fail to provide precise and well-timed diagnoses, and the pathophysiology of MCI is not fully understood. Alterations of serum N-glycan expression could represent essential contributors to the overall pathophysiology of neurodegenerative diseases and be used as a potential marker to assess MCI diagnosis using non-invasive procedures. Herein, we undertook an LC-MS/MS glycomics approach to determine and characterize potential N-glycan markers in depleted blood serum samples from MCI patients. For the first time, we profiled the isomeric glycome of the low abundant serum glycoproteins extracted from serum samples of control and MCI patients using an LC-MS/MS analytical strategy. Additionally, the MRM validation of the identified data showed five isomeric N-glycans with the ability to discriminate between healthy and MCI patients: the sialylated N-glycans GlcNAc₅,Hex₆,Neu5Ac₃ and GlcNAc₆,Hex₇,Neu5Ac₄ with single AUCs of 0.92 and 0.87, respectively, and a combined AUC of 0.96; and the sialylated-fucosylated N-glycans GlcNAc₄,Hex₅,Fuc,Neu5Ac, GlcNAc₅,Hex₆,Fuc,Neu5Ac₂, and GlcNAc₆,Hex₇,Fuc,Neu5Ac₃ with single AUCs of 0.94, 0.67, and 0.88, respectively, and a combined AUC of 0.98. According to the ingenuity pathway analysis (IPA) and in line with recent publications, the identified N-glycans may play an important role in neuroinflammation. It is a process that plays a fundamental role in neuroinflammation, an important process in the progression of neurodegenerative diseases.

Keywords: MCI; biomarker glycan; isomeric N-glycans; neurological disorder.

Figure 1

Workflow for the LC-MS analysis and glycan nomenclature. A four-digit N-glycan nomenclature was used in the next order; N-acetylglucosamine, hexose, fucose, and N-acetylneuraminic acid (GlcNAc, Hex, Fuc, Neu5Ac).

Figure 2

Butterfly plot comparison of the N-glycan profiles observed for the for MGC-LC-MS (A), C18(50 cm)-LC-MS (B) analysis; the green color represents the abundance of control samples and the red color the abundance of the MCI samples. Glycan identification as in Figure 1.

Figure 3

Bar graph compares the % glycan abundance between techniques, and the Venn diagram compares the number of N-glycans detected for each strategy.

Figure 4

Changes in the glycome abundance of control and MCI samples. (A) MGC-LC-MS and (B) C18(50cm)-LC-MS approaches.

Figure 5

Score plots comparing glycomics patterns from control and MCI patients. (A) PCA, R2X[1] = 0.599, R2X[2] = 0.142, Ellipse Hotelling’s T2 95% CI. (B) PLS-DA, R2X[1] = 0.592, R2X[2] = 0.122, Ellipse Hotelling’s T2 95% CI. (C) OPLS-DA, Scaled proportionality to R2X, R2X[1] = 0.435, R2X₀[1] = 0.278, Ellipse Hotelling’s T2 95% CI.

Figure 6

Dot plots and figures of the N-glycans with statistically significant differences between control and MCI samples. Glycan nomenclature: GlcNAc, Hex, Fuc, NeuAc (N-acetylglucosamine, Hexose, Fucose, N-acetylneuraminic acid).

Figure 7

ROC curves of the N-glycans with statistically significant differences between control and MCI samples. Single ROC curves for the up regulated (A) and down regulated (B) N-glycans. Combined ROC curves for the up regulated (C) and down regulated (D) N-glycans. Glycan nomenclature: GlcNAc, Hex, Fuc, NeuAc (N-acetylglucosamine, Hexose, Fucose, N-acetylneuraminic acid).