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. 2025 Dec 4;11(1):1883–1897. doi: 10.1021/acsomega.5c10061

Predictive N‑Glycan Signatures of Severe Traumatic Brain Injury in Biofluids Using LC–MS/MS

Joy Solomon †, Sherifdeen Onigbinde †, Moyinoluwa Adeniyi †, Oluwatosin Daramola †, Cristian Gutierrez-Reyes †, Mojibola Fowowe †, Md Mostofa Al Amin Bhuiyan †, Judith Nwaiwu †, Firas H Kobeissy ‡, Stefania Mondello §, Ava M Puccio ∥, Yehia Mechref †,*
PMCID: PMC12809573  PMID: 41552607

Abstract

Traumatic brain injury (TBI) poses a significant global health issue, frequently resulting in persistent and even lifelong cognitive and neurological impairments. Despite remarkable advances in biomarker discovery, significant challenges remain in the accurate diagnosis and prognosis of TBI. Glycosylation, an important post-translational modification of proteins and other biomolecules, plays an essential role in neuronal function and neuroinflammation. However, its contribution to the pathogenesis of TBI has been insufficiently investigated. This study examines changes in N-glycosylation patterns in serum and cerebrospinal fluid (CSF) from individuals with severe traumatic brain injury (sTBI) at various time points postinjury. Employing advanced glycomics methodologies and liquid chromatography–tandem mass spectrometry (LC–MS/MS), we identified 102 N-glycans in serum and 86 N-glycans in CSF, revealing substantial alterations in N-glycan expression, including differential expression of fucosylated and sialylated structures. Elevated fucosylation was observed in serum, whereas decreased fucosylation was found in CSF. Altered sialylation patterns were noted, suggesting glycosylation alterations in neuroinflammatory processes and possible neurodegeneration. Furthermore, our study examined N-glycans with isomeric properties. We identified several isomers that demonstrated potential as a biomarker panel reflective of TBI progression. Overall, these studies offer new insights into systemic and central nervous system-specific glycomic responses following sTBI and emphasize the potential of glycan-based biomarkers for monitoring specific changes as TBI progresses, which could be a possible target for enhanced TBI therapy and enhanced prognosis.

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Introduction

Traumatic brain injury (TBI) is a complex pathological condition and a leading global cause of mortality and morbidity, accounting for over 30% of fatalities due to injuries in the United States. , TBI is a dynamic process that encompasses both immediate and delayed pathological responses. The primary injury occurs instantaneously during trauma, directly damaging the glial cells, neurons, and blood vessels. Secondary injury initiates a cascade of molecular and cellular events that can persist for weeks or months, leading to neuronal and astrocytic dysfunction, axonal disconnection, and widespread neuroinflammation. − This protracted timeline of injury progression necessitates a deeper comprehension of the underlying mechanisms to develop effective neuroprotective and therapeutic interventions. The Glasgow Coma Scale (GCS) is the most used clinical tool for assessing the initial severity of TBI. It categorizes injuries into three levels: mild, moderate, and severe, with scores of 13–15, 9–12, and 3–8, respectively. Mild TBI (mTBI) often presents with symptoms such as headache and impaired concentration. In contrast, more severe cases may involve symptoms ranging from seizures and amnesia to coma.

Despite advancements in imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) in TBI diagnosis, they have limitations, particularly in evaluating the full spectrum of TBI pathologies. CT imaging, while rapid and widely available, often fails to detect subtle parenchymal injuries, diffuse axonal injuries (DAI), and early signs of cerebral ischemia or intracranial hypertension. , MRI, although superior for identifying microstructural and functional brain changes, is limited by accessibility, cost, and the extended time required for imaging. , Both modalities struggle in prognostication and in capturing the dynamic nature of secondary injuries, such as hypoperfusion or progressive neurodegeneration, which are critical in understanding and managing TBI. , Fluid biomarkers offer a more accessible alternative and are increasingly recognized as crucial tools for understanding the complex pathophysiological changes after TBI. Nevertheless, current biomarkers face limitations due to low concentrations, restricted passage across the blood–brain barrier (BBB), and altered cerebrospinal fluid (CSF) dynamics postinjury. , These challenges create a pressing need to identify and validate novel biomarkers to reliably diagnose TBI, stratify its severity, and predict long-term outcomes.

N-linked glycans, one of the most structurally diverse classes of complex carbohydrates, play pivotal roles in mammalian cell physiology. Their multistep synthesis, involving hundreds of glycosyltransferases and glycosidases, is intricately linked to protein folding, stability, trafficking, and function. , N-glycans play an essential role in brain physiology through modulation of a range of neuronal processes, including synaptic plasticity, neuronal morphology, and basal glial cell homeostasis. , These glycan types are involved in critical functions such as maintaining resting membrane potential, neurotransmitter release, and synaptic vesicle trafficking, highlighting their importance in neuronal signaling. ,, Alterations in N-glycans and their isomers have been reported in diverse neurological conditions including Alzheimer’s, Parkinson’s disease, mild cognitive impairment, narcolepsy type 1, and others. − Glycomic alterations are gaining recognition for their role in brain injury and repair processes. Glycans have been shown to influence neuroinflammation, a critical response to brain injury, by modulating the activity of microglia and astrocytes, the primary immune cells of the central nervous system. The interplay between glycosylation and inflammatory responses may provoke neuronal damage and contribute to long-term neurodegenerative processes. Understanding the role of N-glycans and their isomers in the pathology of severe traumatic brain injury (sTBI) could offer critical insights into the biochemical changes associated with the condition. Such knowledge could uncover pathways for therapeutic intervention, thereby advancing treatment strategies.

In comprehensive glycomics studies, liquid chromatography combined with tandem mass spectrometry (LC–MS/MS) has become the analytical method of choice owing to its sensitivity, specificity, structural characterization, and compatibility with derivatization techniques such as permethylation. Mesoporous graphitized carbon (MGC) columns further enable the separation of isomeric glycans. Hence, in this pilot study, we performed a detailed N-glycomic profiling of serum and CSF samples from sTBI patients collected at days 1, 3, and 5 postinjury, compared with healthy controls. We aimed to characterize glycosylation dynamics and identify novel biomarker candidates to advance the understanding of injury-associated alterations and improve prognosis.

Materials and Methods

Sample Classification

Serum and cerebrospinal fluid (CSF) samples were obtained from patients enrolled in the Brain Trauma Research Center (BTRC) repository, from a single site Level 1 trauma center (University of Pittsburgh, IRB no. 19030228, PI Puccio). Inclusion criteria for the larger repository are (1) age between 16 and 80 years, (2) diagnosis of sTBI with Glasgow Coma Scale (GCS) score of 4–10 and a positive head computerized topography (CT) for intracranial injury, (3) placement of an external ventricular drain (EVD) for intracranial monitoring and CSF drainage per standard of care, and (4) signed written consent provided by a legal authorized representative. Exclusion criteria included pregnancy or imminent brain death. This small cohort is from participants enrolled from 2018 to 2021. Samples were obtained at three time points: day 1 (N = 6), day 3 (N = 8), and day 5 (N = 5) postinjury (as shown in Table ), totaling 13, from distinct sTBI patients. For eight patients, samples were collected at a single time point (day 1, 3, or 5), while four patients had samples collected at two time points. One patient provided samples at all three time points. The Glasgow Outcome Scale-Extended (GOS-E) score was used to assess neurological outcome, dichotomized as poor outcome (GOS-E 1–4) and favorable outcome (GOS-E 5–8).

1. Demographic and Clinical Characteristics of TBI Patients and Controls Included in Serum and CSF Analyses .

Serum Samples Control Day 1 Day 3 Day 5
Number 19 6 8 5
Age in years (mean ± SD) 41 ± 16 41 ± 14 43 ± 15 40 ± 26
Gender (M/F) 16/3 5/1 7/1 4/1
GCS Neurosurgery   4–7(T) 3–8(T) 3–10(T)
GOS-E range (2 weeks)   2–3 2–3 2
GOS-E range (12 weeks)   3–5 3–5 1–5
Diabetes (yes/no) 0/19 1/5 1/7 1/4
Cancer (yes/no) 0/19 0/6 0/8 1/4
CSF Samples control day 1 day 3 day 5
Number 19 6 8 5
Age (mean ± SD) 40 ± 14 41 ± 14 43 ± 15 40 ± 26
Gender (M/F) 16/3 5/1 7/1 4/1
GCS Neurosurgery   4–7(T) 3–8(T) 3–10(T)
GOS-E range (2 weeks)   2–3 2–3 2
GOS-E range (12 weeks)   3–5 3–5 1–5
Diabetes (yes/no) 0/19 1/5 1/7 1/4
Cancer (yes/no) 0/19 0/6 0/8 1/4
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a

Serum and CSF samples were collected from healthy controls (n = 19) and TBI patients at Day 1 (n = 6), Day 3 (n = 8), and Day 5 (n = 5) postinjury. The mean age (±SD), gender distribution (male/female), Glasgow Coma Scale (GCS) scores, and Glasgow Outcome Scale Extended (GOSE) ranges at 2 and 12 weeks post-injury are reported for each group. GCS values are expressed as ranges, with “T” denoting intubated patients. At enrollment, patients presented with severe TBI. Functional outcomes were assessed longitudinally using the GOSE scale: most patients were in a vegetative state or lower severe disability at 2 weeks, while 12 week outcomes varied from death to lower moderate disability. Individual subject-level data highlight the clinical heterogeneity of TBI patients across time points. Clinical data (diabetes, cancer status) are also included.

At 2 weeks postinjury, all patients exhibited GOS-E scores ranging from 2 to 3. After 12 weeks, scores improved slightly by 1–3 points. Additionally, 19 serum and 19 CSF samples were collected from healthy individuals as controls. The serum control samples were obtained from the Center of Neurotrauma, Multiomics & Biomarkers (CNMB) Biorepository, BIOIVT study. The CSF control samples were purchased from GoldenWest BioSolutions LLC.

Chemicals and Reagents

Iodomethane (CH3I), ammonium bicarbonate (ABC), acetic acid, the borane–ammonia complex, formic acid (FA), sodium hydroxide (NaOH) beads, and dimethyl sulfoxide (DMSO) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Enzyme PNGase F was obtained from New England Biolabs (Ipswich, MA, USA), and difluoroacetic acid (DFA) was acquired from Acros Organics (New Jersey, USA). HPLC-grade isopropyl alcohol (IPA), water, acetonitrile (ACN), and methanol (MeOH) were obtained from Fisher Scientific (Fair Lawn, NJ, USA). The microspin columns used for permethylation were sourced from Harvard Apparatus (Holliston, MA, USA), while the Isolute C18 (EC) solid-phase extraction (SPE) columns were obtained from Biotage (Charlotte, NC, USA).

Release and Purification of N-Glycans from Serum and CSF Biofluids

Protein concentrations of serum and CSF samples were measured using a bicinchoninic acid (BCA) protein assay kit. Aliquots corresponding to 30 μg of the starting material were transferred into 1.5 mL Eppendorf tubes. All of the samples were then diluted with 50 mM ABC buffer to a final volume of 50 μL, vortex-mixed, and spun down. Proteins were denatured by heating at 90 °C for 15 min, followed by the addition of 1000 U PNGase F and incubation at 37 °C for 18 h to release N-glycans. Postdigestion, the samples were dried in a Labconco vacuum concentrator and subsequently reconstituted in 300 μL of 5% acetic acid to purify the released N-glycans. This step was necessary to remove deglycosylated proteins using a solid-phase extraction (SPE) cleanup method with C18 cartridges. The SPE cartridges were preconditioned with 1 mL of methanol three times and then equilibrated with 1 mL of 5% acetic acid three times. The resuspended samples were then loaded onto the SPE columns and washed three times by adding 300 μL of 5% acetic acid. The collected flowthrough containing the N-glycans was dried using a vacuum concentrator.

Reduction and Permethylation of N-Glycans for Enhanced Detection

The purified N-glycans underwent a reduction process following a previously established protocol. A fresh borane–ammonia solution (10 μg/μL in water) was added (10 μL) to each sample and incubated at 60 °C for 1 h. To remove excess borane-amine, methanol (1000 μL) was repeatedly added five times, and the resulting methyl borate was evaporated using a vacuum concentrator. After reduction, the N-glycans were permethylated using a previously reported solid-phase protocol. To the reduced N-glycan samples, 30 μL of DMSO was added, followed by the addition of 1.2 μL of water and 20 μL of iodomethane. Microspin columns preloaded with NaOH beads suspended in DMSO were spun at 1800 rpm for 2 min and subsequently washed with an additional 200 μL of DMSO before another spin at the same speed. The prepared sample solution was then introduced into the spin columns and incubated in the dark at room temperature for 25 min. Another 20 μL of iodomethane was added, followed by a 15 min incubation. Eluates were collected by centrifugation, with 30 μL of ACN added to enhance elution. Finally, the permethylated N-glycans were dried and reconstituted in an aqueous solution containing 20% ACN and 0.1% formic acid, preparing them for LC–MS analysis.

RPLC-MS/MS Conditions for N-Glycan Analysis Using a 150 mm C18 Column (C18-LC-MS/MS)

A comprehensive analysis of N-glycans extracted from serum and CSF samples obtained from TBI patients was conducted across multiple time points with comparisons to non-TBI controls. For LC–MS/MS analysis, 1 μg of reduced and permethylated N-glycan samples was injected into the system. The analysis was carried out by utilizing an UltiMate 3000 Nano UHPLC system coupled to an Orbitrap Fusion Lumos mass spectrometer (Thermo Scientific, San Jose, CA, USA) operated in the positive ion mode. Samples were loaded onto an Acclaim PepMap 100 C18 trapping column (75 μm × 2 cm, 3 μm, 100 Å) for online purification at a 3 μL/min flow rate of mobile phase A (MPA) for 10 min. Chromatographic separation was performed using a reversed-phase C18 Acclaim PepMap analytical column (15 cm × 75 μm). The mobile phases consisted of mobile phase A (MPA: 98% water, 2% acetonitrile (ACN), 0.1% formic acid (FA)) and mobile phase B (MPB: 98% ACN, 2% water, 0.1% FA). The gradient elution was carried out at 55 °C with a flow rate of 0.35 μL/min, starting at 20% mobile phase B for 10 min, increasing to 55% over 35 min, ramping to 90% within 5 min, and then returning to 20% for column equilibration over 5 min. Following chromatographic separation, permethylated glycan samples were analyzed by using the Orbitrap Fusion Lumos mass spectrometer. The electrospray ionization (ESI) source was set to a 2 kV spray voltage with a capillary temperature of 305 °C. Full-scan acquisition was performed at 120 k resolution with an AGC target set to standard, a maximum IT set to Auto, and a scan range of 400–2000 m/z. Tandem MS/MS data were collected in a DDA setup, selecting the top 20 most intense precursor ions for fragmentation. Quadrupole isolation was performed using a 2 m/z isolation window, and fragmentation was achieved with a fixed collision-induced dissociation (CID) energy of 35. The tandem MS/MS scans were acquired at 30,000 resolutions, using AGC and Max IT settings maintained as in the full scan mode.

Conditions for LC–MS/MS Analysis of N-Glycan Isomers Using an MGC Column (MGC–LC–MS/MS)

To profile N-glycan isomers associated with TBI, an in-house packed 10 mm mesoporous graphitized carbon (MGC) column was utilized, which has been shown to enable efficient isomeric separation of permethylated glycans. For this analysis, an UltiMate 3000 Nano UHPLC system (Thermo Scientific, San Jose, CA, USA) was coupled to a Q-Exactive HF mass spectrometer (Thermo Scientific, San Jose, CA, USA) operating in the positive ion mode. All samples were subjected to a 90 min multistep gradient for optimal glycan separation. MPA consisted of 98% water, 2% ACN, and 0.1% difluoroacetic acid (DFA), while MPB was composed of 50% ACN, 50% isopropanol (IPA), and 0.1% DFA. The column temperature was set at 75 °C, with a constant flow rate of 0.3 μL/min. The gradient program began at 20% MPB for 10 min, gradually increasing to 60% over 20 min, followed by a ramp to 95% over 30 min. The column was held at 95% MPB for 20 min, decreased to 20% over 8 min, and equilibrated for 2 min under the initial conditions. The separated N-glycans were analyzed using a nano ESI source, also in the positive mode. The spray voltage used was 1.6 kV, and the temperature of the transfer tube was maintained at 275 °C. The full MS spectra were acquired in an Orbitrap within a mass range of 400–2000 m/z. The Orbitrap resolution used was 120 k, at 5 ppm mass accuracy. We used a maximum injection time of 50 ms and an AGC target of 1 × 106. MS/MS data were collected in the DDA mode, selecting the 20 most abundant precursor ions for fragmentation. A normalized collision energy (NCE) of 23% was used to fragment precursor ions that were selected within an isolation window of 2 m/z. We set the AGC target for MS/MS acquisition at 1 × 105, and the Orbitrap resolution was configured to 30 k. Lastly, the maximum IT for MS/MS scans was 100 ms with a loop count of 20.

Data Processing, Validation, and Statistical Analysis of N-Glycans

Raw data files were manually reviewed and validated using Xcalibur software (Version 4.2, Thermo Scientific), ensuring accurate retention time alignment, monoisotopic mass confirmation, and MS/MS spectral verification within a 5 ppm mass tolerance. Quantification of N-glycan peak areas was conducted using Skyline software (Version 23.1.0.380) to determine glycan abundances. Additionally, theoretical validation of fragment ions was performed using GlycoWorkbench (Version 2.1). Normalized abundance values for each peak were obtained using the total peak abundance. Prior to statistical testing, data normality was evaluated using the Shapiro–Wilk test and visual inspection through the Q–Q plots. As the majority of variables deviated significantly from a normal distribution (p < 0.05), nonparametric statistical methods were adopted. Accordingly, statistical differences between the control and postinjury groups were assessed using the Wilcoxon rank-sum test with Benjamini–Hochberg (BH) correction for false discovery rate control. Data visualization and statistical analyses were performed in R (v4.4.1). Origin Pro software (Version 10.2.0.196) was used for the unsupervised 3D principal component analysis (PCA). Biorender software was utilized to create workflow schematics for graphical representation. A post hoc power analysis (α = 0.05, target power = 0.8) was conducted to evaluate the statistical power of the current sample size and to estimate the minimum number of samples required in future studies to achieve 80% power across effect sizes of 0.7, 0.8, and 0.9 for each serum and CSF comparison.

Results and Discussion

N-Glycan Analysis Workflow for TBI Samples

As shown in Figure , a uniform sample protein concentration was denatured at 90 °C for 15 min and digested at 37 °C for 18 h with PNGase F. The N-glycan purification separated the N-glycans from proteins after incubation. To improve ionization, the released N-glycans were reduced and permethylated. Two LC–MS/MS methodologies and analyses were used in this study to investigate permethylated N-glycans. The first analysis was an untargeted chromatographic separation with a 150 mm C18 column carried out in 60 min. The second method quantified isomeric N-glycans released from both biofluids to characterize their changes using a 10 mm in-house-packed MGC column. After the analysis, N-glycan structures were identified and validated by using suitable software. The N-glycan structures studied are denoted by four-digit nomenclature, where each digit represents the number of specific monosaccharide units. For example, a structure of “4511” would represent four (4) N-acetylglucosamine residues (HexNAc), five (5) hexose residues (Hex), one (1) fucose residue (Fuc), and one (1) N-acetylneuraminic acid residue (NeuAc). The Figure caption shows the symbols for each of the composing monosaccharide units. In the isomeric section, the asterisk after the symbol “_” denotes the isomer of the N-glycan structure. For example, 5602_5 denotes the fifth isomer of N-glycan 5602.

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Schematic representation of the N-glycomics workflow. The biofluids were denatured, followed by digestion using PNGaseF, reduction, and permethylation. The permethylated N-glycans were analyzed utilizing advanced LC–MS/MS techniques. Glycans were identified using Xcalibur software and quantified using Skyline software. The N-glycan symbols used in this work include blue square solid GlcNAc; green circle solid Mannose, yellow circle solid Galactose; red triangle left-pointing solid Fucose; and magenta diamond solid NeuAc.

Characterization and Differential Expression of N-Glycans in Biofluids from TBI Patients

Glycan profiling has emerged as a powerful approach in biomarker discovery, offering potential for disease management and therapeutics. These blood-based biomarkers have the potential to identify pathophysiological differences in individuals, paving the way for personalized management. , Advancements in highly sensitive analytical techniques, such as mass spectrometry, have significantly increased our understanding of the diversity and heterogeneity of glycans and their isomers, , thereby facilitating the elucidation of the concealed system at the glycome level. Given their crucial significance, N-glycans from biofluids of TBI patients have been characterized by the present study, which could yield valuable insights into the function of these glycans in TBI. The classification of TBI patients and controls, including demographic and clinical characteristics (age, gender, GCS at admission, and GOSE scores at follow-up), is summarized in Table , highlighting the heterogeneity of the study cohort.

By utilizing the C18-LC-MS/MS method, we identified a total of 102 N-glycans in serum, while in CSF, we identified 86 N-glycans. To identify the differential expression of the N-glycans between the different time points postinjury and the controls and to show the quantitative differences in the data, the different cohorts were analyzed and compared using the unsupervised Principal Component Analysis (PCA), as shown in Figures and S1, which were plotted using the relative abundances of the identified N-glycans. PCA facilitates the visualization and identification of similarities and patterns among different data sets. The disparities among the cohorts are more apparent as the PCA distance grows. Observed clustering reveals variations in N-glycan expression across the different cohorts. The 3D plot, as shown in Figure S1, was conducted unsupervised using Origin Pro software with 95% confidence. Good separation and clustering within groups were observed in the serum compared to in the CSF. Subsequently, we examined the 2D plots, comparing the distinct days with the control, thereby demonstrating the variation of the distinct days as to the controls. The observed discrepancies in the PCA conducted among the different comparisons in the serum are displayed in Figure a–c, while Figure d–f shows the observed differences in the CSF. Greater disparities were noted in the serum 2D PCA relative to CSF PCA, as evidenced by the 3D plot shown in Figure S1. Among the serum sample comparison, the most notable differences were found between the Day 1 and Control groups, as shown in PC1 (40.93%), potentially reflecting the acute response within the first 24 h after injury. Separation was mostly observed in PC2 for Day 3 and Day 5 comparison for serum. The most significant alteration noted in the CSF was most evident in the comparison between Day 3 and the control group. Comparing the different time points postinjury, we observed some visible separation, as shown in Figure g–i for serum and Figure j–l for CSF. For the serum, Day 1 and 5 showed the most separation along PC1 (41.96%). Minimal changes were observed in the CSF different time points comparisons. Overall, these PCA results demonstrate the responsiveness of the serum and CSF to TBI-induced pathophysiology, with the serum N-glycome being highly responsive to acute TBI. To provide additional quantitative overview of the N-glycan features driving the observed PCA separations, loading plots showing the corresponding N-glycans loadings (vectors) for each comparison are provided for all analyses in both serum and CSF (Figure S2). Although multiple glycans contributed to the observed variance, to reduce complexity, only the top 15 N-glycans contributing the most strongly to the overall variance in each PCA were plotted along with their percentage contributions to PC1 and PC2. These loading plots quantitatively illustrate how specific N-glycans influence the direction and magnitude of separation between cohorts. Across all comparisons, several N-glycans consistently contributed substantially to PC1 and PC2, highlighting their importance in driving the differences between postinjury and control samples. Together, these loading plots complement the PCA score plots by revealing both the extent of group separation and the identity and contribution of the N-glycans responsible for it, thereby providing a clear depiction of the variables underlying the observed clustering patterns. Studies have shown CSF biomarkers to offer the most accurate representation of the central nervous system pathobiological processes in TBI. The CSF provides a pathway in waste elimination from the brain, including the elimination of proteins and other debris during injury recovery or microhemorrhage. Less overall separation in the PCA for the CSF may be ascribed to several potential hypotheses. However, further investigation may explain the reduced variation observed in the CSF PCAs compared with that in the serum.

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2D principal component analysis with 95% confidence of all identified N-glycans in the serum and CSF of TBI patients comparing the different time points postinjury and control. (a) Serum control vs Day 1, (b) serum control vs Day 3, (c) serum control vs Day 5, (d) CSF control vs Day 1, (e) CSF control vs Day 3, (f) CSF control vs Day 5 (g) serum Day 1 vs Day 3, (h) serum Day 1 vs Day 5 (i) serum Day 3 vs Day 5, (j) CSF Day 1 vs Day 3, (k) CSF Day 1 vs Day 5, and (l) CSF Day 3 vs Day 5.

Figure depicts representative extracted ion chromatograms (EICs) of N-glycans identified in the different time points postinjury and control group. Figure a depicts the representation for the serum, and Figure b depicts the representation for the CSF, including a representative spectrum from a cohort in the different biofluids. To assess differences in overall N-glycan profiles among Day 1, Day 3, Day 5, and control groups, the relative expression levels of all identified N-glycans were quantified and are detailed in Supplementary Tables S1 and S2.

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Representative extracted ion chromatograms displaying selected identified N-glycans in (a) serum and (b) CSF. Symools as in Figure

Glycosylation Patterns and N-Glycan Subtype Expression

Glycan structures are not directly encoded by genes; instead, they are modified by a network of enzymes in a template-independent manner. This results in heterogeneity at two levels: site occupancy and structural diversity at occupied sites. Their biosynthesis begins in the endoplasmic reticulum, which is a more conserved step, and then, trimming by glycosidases continues in the Golgi apparatus. The maturation of Golgi N-glycans involves a wide array of glycosidases and glycosyltransferases, resulting in a variety of N-glycans. This process endows eukaryotes with an intricate combinatorial system, producing a wide array of N-glycan structures without requiring preceding genomic modifications. To investigate the variations in N-glycome profiles across the groups, the identified N-glycans were classified based on their monosaccharide compositions and structural characteristics attached to the N-glycan core, which influence their biological functions and interactions, thereby providing insight into their structural diversity and functional significance. The glycans were grouped into distinct categories, including sialylated, fucosylated, sialofucosylated, high-mannose, and neutral glycans. A bar chart depicting the distribution of these N-glycan types across the analyzed groups is presented in Figure . Notably, fucosylated N-glycans were predominantly observed in CSF, whereas sialylated glycans were more prevalent in serum. In the literature, complex structures such as fucosylated and sialylated structures have been shown to be in high abundance in the serum, which correlates with our data. The predominant glycan identified in the serum was a biantennary, fully sialylated complex N-glycan with the composition 4502. Statistical analysis (p < 0.05) revealed significant differences in glycan types across the cohorts, particularly in serum. Day 1 exhibited significant alterations, including increased fucosylation in the serum, whereas CSF showed a nonsignificant decrease of fucosylated N-glycans. Additionally, analysis of glycan features revealed a decrease in sialofucosylated glycans on Day 1 compared to the control, followed by an increase over time when the other days were compared to Day 1 in the serum (as shown in Figure a). Similar trends were also observed for other glycan types. In CSF, a significant difference in sialylated glycans was observed between Day 3 and the control group (as shown in Figure b).

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Bar graphs showing the glycan profiles of all identified N-glycans in (a) serum and (b) CSF.

Second to sialic acid, fucose is found in 7.2% of oligosaccharides, thereby making it a common modification on proteins and lipids. Core fucosylation has been linked to regulation of neuroinflammation and glial cell activation. Enzyme α1,6-fucosyltransferase (Fut8), which transfers fucose to the innermost GlcNAc residue through an α1,6-linkage, plays essential roles in immune regulation. Dysregulated Fut8 activity has been implicated in tumor formation, central nervous system diseases, and inflammatory and immune responses. For this study, we observed a downregulation of fucosylated N-glycans in the CSF and a significant upregulation of fucosylated glycans in the serum. To expand on the fucosylated N-glycans that could potentially cause significant alterations, the fucosylated glycans were divided into different types based on the number of fucose units present, classifying them into mono-, bi-, and trifucosylated glycans. The major changes were observed to be driven by monofucosylated glycans (Figure S3a,b). A similar fucosylation trend was observed in both biofluids after classification. The identification of N-glycan composition was achieved through full MS and MS2 characterization. Hence, to confirm that core-fucosylated N-glycans contain fucose in their core structure, the MS2 spectra were examined for diagnostic ions specific to core N-glycans. The composition of the N-glycans was first confirmed by full MS (insets), as shown in Figure S4a,b, followed by MS2 analysis, which provides more structural information for the specific positions of the monosaccharides. Because core fucosylation produces a diagnostic ion at m/z 468.2803, which is different from branch fucosylation, MS2 spectra were examined for this ion. Representative core-fucosylated glycans, including 4310 and 4512, exhibited this diagnostic fragment (Figure S4a,b), confirming their core-fucosylated nature and suggesting possible roles in TBI-related pathological processes.

The specific function of a glycan is typically dictated by its terminal sugar residues. Sialic acid has been associated with microdomain formation, tissue homeostasis, cell adhesion, cell migration, and chemokine sensing − and overall plays a vital role in brain functions. Several innate immune receptors and proteins directly recognize sialic acids as regulatory checkpoints of immune activity. In this study, opposite trends were observed in sialylation across both biofluids. Sialylated N-glycans decreased in the serum, while an increase in sialylated N-glycans was observed in the CSF, as shown in Figure . Comparing the different days in the serum, an increase was observed in Days 3 and 5 when compared with Day 1. This could be indicative of increased neuroinflammation as the condition progresses. Subsequently, we classified the sialylated N-glycans into mono-, di-, tri-, and tetra- subtypes based on the number of sialic acid units they contain, as shown in Figure S3c,d. Significant alterations were influenced by both monosialylated and disialylated N-glycans in the serum. According to this subtype classification, the monosialylated N-glycans in the CSF followed the same upregulation trend. The opposite trend seen in Figure was observed in the disialylated N-glycans in serum, which indicated that they predominantly influenced the observed trend. Specifically investigating CSF, the significantly increased sialylation levels could be indicative of their role in secondary injury processes following a sTBI. In the serum, sialylated N-glycans decreased and fucosylated N-glycans increased. We then observed that the sialofucosylated N-glycans followed trends similar to those of the sialylated ones, which could possibly indicate an influence of sialylation on the fucose units. Studies have shown that the CNS has the highest concentration of sialic acids. This is thereby indicative of their roles in brain injury progression.

High-mannose N-glycans followed a similar trend of upregulation in both biofluids when comparing the different time points postinjury to the controls, as shown in Figure . In addition to the fucosylated and sialofucosylated structures that are found in abundance in the brain, high-mannose structures have also been reported to be in high abundance. These have been investigated for their role in brain development and have been identified in neuronal synapses. A higher serum and CSF concentration of high-mannose N-glycans postinjury could be considered as a possible biomarker of brain injury. Their relevance can be studied with a sole focus on the CSF as they have a direct connection to the activities that take place there. High-mannose N-glycans in the CSF showed a continuous rise over the different time points, with the largest increase observed between Day 5 and control. The observed upregulation across the time frame could be attributed to alterations in the glycosylation pattern of proteins secreted in the CSF.

Our previous study has shown distinct brain structures, such as 5300 and 5401, to be upregulated in the serum of TBI patients with unfavorable outcomes (GOS-E ≤ 4). In this current study, 5300 distinct brain structures also demonstrated significant overexpression across the days in contrast to controls in serum. No significant changes were observed in the 5401 N-glycan in serum. Having identified both N-glycans in the CSF, they showed no statistical significance. Delving into more brain-specific glycans in CSF could offer significant insights into alterations associated with sTBI. Investigating the CSF glycome is particularly crucial because of its fluid distinctiveness and its neurological and biological importance, portraying global brain neurochemistry. The uniqueness of the glycoproteins in CSF arises from the diffusion of proteins and peptides from the blood over the blood–CSF barrier together with the synthesis and transport of proteins in the choroid plexus to the CSF from the bloodstream. Hence, they offer a unique supply of proteins that may illustrate alterations in the CNS that transpire during an injury process.

Our previous study also indicated an elevation in serum levels of the tetraantennary complex N-glycan “8600”, as supported by the corresponding MS/MS spectrum (Figure S5), in patients with favorable outcome (GOS-E > 4) but not present in those with unfavorable outcomes. In the present study, however, 8600 was decreased in the serum of sTBI patients across all time points. As the patients assessed here had GOS-E ≤ 5 within 12 weeks after injury, they were temporarily classified within the unfavorable outcome group. This finding is, therefore, the opposite of what was previously reported for favorable outcomes. Notably, 8600 was not detected in the CSF. These observations highlight the importance of examining individual glycans to gain deeper insights into their roles in neurological health and disease.

Identification of Significantly Altered N-Glycans in sTBI Biofluids

Volcano plots are shown in Figure a–c, representing the expression of the N-glycans in the serum of different cohort comparisons. Comparing Day 1 to control, we had 76 significant N-glycans, where 36 were downregulated and 40 were upregulated; for Day 3 and control, we had 59 significant N-glycans, where 27 were downregulated and 32 were upregulated; and comparing Day 5 to the control, we had 55 significant N-glycans with 15 downregulated and 40 upregulated. A drop in significance was observed in the Day 5 comparison. Figure d depicts a Venn plot illustrating the common and significant N-glycans across different comparisons. From the plot, 44 N-glycans were shared among the three comparisons, with 13 unique to Day 1 comparison, 2 unique to Day 3 comparison, and 6 unique to Day 5 comparison. To further explore the N-glycan expression patterns, a heatmap was generated (Figure e) to visualize common N-glycans between time points and controls. The color gradient, from bright red to green, represents upregulation and downregulation, respectively. Rows correspond to statistically significant N-glycans, while columns represent individual samples from different time points and controls. Distinct trends and clustering patterns in N-glycan expression highlight alterations in molecular interactions and regulatory networks in response to sTBI.

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Statistically significant N-glycans in serum comparisons. (a–c) Volcano plots showing the expression of the N-glycans. Green represents the downregulated. Red represents the upregulated. Gray represents the nonstatistically significant glycans. (d) Venn plot showing unique and overlapping significant N-glycans across the different cohorts. (e) Heatmap representation of significant N-glycans common to all comparisons.

Figure a–c depicts the volcano plots representing the N-glycans in the CSF of the different comparisons. Comparing Day 1 to the control, we had 9 significant N-glycans, where 6 were downregulated and 3 upregulated; for Day 3 and control, we had 10 significant N-glycans, where 9 were downregulated and 1 was upregulated; and comparing Day 5 to the control, we had 4 significant N-glycans with 3 downregulated and 1 upregulated. Two N-glycans were common to all comparisons, as shown in Figure d; they are both sialofucosylated N-glycans, downregulated across all comparisons. The heatmaps, as shown in Figure e, illustrate those significant N-glycans present in at least one of the comparisons in the CSF.

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Statistically significant N-glycans in CSF comparisons. (a–c) Volcano plots showing the expression of the N-glycans. Green represents the downregulated. Red represents the upregulated. Gray represents the nonstatistically significant glycans. (d) Venn plot showing unique and overlapping significant N-glycans across the different cohorts. (e) Heatmap representation of significant N-glycans in at least one of the comparisons.

Box Plot Representation of Some Statistically Significant N-Glycans Common to Both Biofluids

The glycome differences observed between serum and CSF highlight distinct regulations governing glycosylation in response to brain injury. To further investigate the opposing trends observed in the sialylated and fucosylated glycan types, we analyzed individual glycans within this group. Some glycans exhibited opposite trends across the two biofluids, such as 4310 and 4411. The opposite trends (upregulated in serum and downregulated in the CSF) seen in 4310 and others with same trends such as 5623 (upregulated in both biofluids or downregulated in both biofluids) are illustrated in Figure S6. An upregulation of the fucosylated glycan 4310 was observed in the Day 1 comparison for the serum, while a downregulation was observed for the same glycan in the Day 1 comparison in the CSF, as shown in Figure S6a,d. The same was observed across Days 3 and 5 compared to the controls for N-glycan 4310 in both biofluids. This speaks to the importance of fucosylation in TBI. This variation could suggest biofluid-specific regulation of fucosylation, potentially influenced by differences in enzymatic activity within the CNS, and systemic circulation. These glycan types have also been reported to be involved in neurodegeneration. This specific glycan with the opposite trend was confirmed to be a core fucosylated glycan, as shown in Figure S4a with the diagnostic m/z outlined with a red color. Core fucosylation may be a significant factor to investigate regarding the variations reported in the study of certain N-glycans across both biofluids.

Correlation of Statistically Significant N-Glycans Common to Both Serum and CSF

To explore whether the abundances of specific significant N-glycans were coordinated between serum and CSF postinjury, we computed correlations between matched glycan features. Figure S7 shows a heatmap of these correlations, with blue shades indicating positive associations and red shades indicating inverse associations. Glycans 4510 and 5500 displayed positive correlations, indicating coordinated changes in circulation and the central nervous system. Interestingly, glycans 4310 and 4411 exhibited negative correlations between serum and CSF, suggesting different regulations across the compartments, as stated earlier. These observations show that the correlations between serum and CSF glycans vary, with some glycans exhibiting positive associations, while others display inverse relationships.

Expression Patterns and Structural Variations of N-Glycan Isomers

Given this variability, it is also critical to consider the expression patterns and structural variations of the N-glycan isomers. The heterogeneity of glycans results in the formation of isomeric structures, which arise from the absence of a reaction template during its synthesis. Glycan production does not depend on a direct template-based mechanism, resulting in microheterogeneity. The intricate complexity of the glycan structures is a result of diverse monosaccharide residue compositions, multiple linkages, branching, and potential modification of the glycans with some functional groups. Isomers are molecules with the same molecular formula but with a different arrangement of atoms. These increase the diversity of glycan structures, which is essential for their functional specificity and biological roles. Understanding their impact has significant implications for biology and medicine. Changes in the expression of these isomeric forms have been linked to various diseases. ,, Considering their importance, as there are therapeutic strategies specific to targeting the isomers, we hereby studied their role in sTBI development.

Employing our in-house-developed nano MGC-LC-MS/MS technique, we identified and quantified 86 different N-glycans in the serum, consisting of 47 nonisomeric and 39 isomeric N-glycans. The 39 isomers contributed to a total of 115 N-glycan isomers, making a total of 162 for both isomeric and nonisomeric structures. In the CSF, we identified a total of 87 N-glycans consisting of 46 nonisomeric and 41 isomeric N-glycans. The 41 isomeric N-glycans resulted in 113 N-glycan isomers, thereby making a total of 159 for both isomeric and nonisomeric structures. Supplementary Tables S3 and S4 show the average relative abundance of the glycan isomers identified in both biofluids.

Comparing the identified N-glycans in serum between the MGC and C18 run, we had 71 N-glycans in common, with 31 unique to C18 and 15 unique to MGC, as seen in Figure S8a. For the CSF, as shown in Figure S8b, 68 N-glycans were common, with 18 unique to C18 and 19 unique to MGC. Following statistical tests, the serum revealed 86 notable isomeric N-glycans differentiating Day 1 from the control, with 33 upregulated and 53 downregulated. The comparison between the Day 1 and control groups revealed the most significant changes. Of the 63 N-glycans significant between Day 3 and control, 31 were upregulated and 32 were downregulated. Out of the 56 significant N-glycans between Day 5 and the control, 36 were upregulated and 19 were downregulated. The CSF analysis also identified the most significant change in Day 1 and control comparison, with 52 significant isomeric N-glycans, where 19 were upregulated and 33 were downregulated. Of the 48 differentially expressed between Day 3 and control, 7 were upregulated with 41 downregulated. In Day 5 and control comparison, we had 5 significant N-glycans, where 1 N-glycan was upregulated (6410_5) and the remaining 4 isomeric N-glycans (7500_1, 7500_2, 3310_2, and 4411_3) were downregulated.

Exceptional separation efficiency and sensitivity were observed using the MGC column, which facilitated the effective profiling of N-glycan isomers in serum and CSF samples from TBI patients at different time points and healthy control individuals. This approach provided precise resolution of isomers with identical glycan compositions, encompassing the different N-glycan types, thereby resulting in enhanced identification and quantification. The isomeric separation achieved in both TBI patients from the different injury time points and healthy individuals is shown for 2700 and 4512 in Figure , and a few others (such as 4502 and 5602) are shown in Figure S9. Isomeric changes were observed in all of the N-glycan types. As depicted in Figure a, two isomers were identified for the high-mannose, 2700. Isomer 2700_2 showed a higher abundance, and significant differences were observed. For isomer 2, an increase in expression in Day 1 when compared to the control was observed for both biofluids. Comparing Day 3 and 5 to Day 1, a consistent decrease was observed across, for isomer 2. Figure c displays the extracted ion chromatogram (EIC) of isomeric N-glycan 4512, which comprises four distinct isomers. The elution order of these glycan isomers has been established in cancer cell lines in our prior studies utilizing MGC-MS/MS and PGC-MS/MS. , These isomers align with our findings in the present study. The bar graph (Figure d) beneath the EIC shows a comparison of the different cohorts based on their relative abundance. Isomers with α2,6-linked sialic acid on both arms show a higher abundance in both biofluids. Significant alterations were observed between Day 1 and control and Day 3 and control in the CSF for isomers 1–3; alterations were also observed between Day 5 and control for isomer 1 and Day 1 and control for isomer 4, in serum. Glycans with α2,3 linked sialic acid tend to elute earlier than those with α2,6 linked sialic acid. This correlates with previous studies. In the presence of more sialic acids, the more α2,6 linkages they contain, the later they would elute. The increased abundance of α2,6 linked sialic acid observed here could relate to a significant role in the brain injury process.

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(a) Representative extracted ion chromatograph of 2700 isomers, (b) bar graphs of 2700 isomers, (c) representative extracted ion chromatograph of 4512 isomers, and (d) bar graphs of 4512 isomers between the different comparisons in both biofluids. Symbols as in Figure .

Although four isomers have previously been reported in cancer cell lines, two isomers were observed for 4502 in this study, as shown in Figure S9a. It has been shown that α2,3-linked sialic acid elutes prior to α2,6, thus justifying the assigned designation of the isomers. The bar graph beneath the EICs (Figure S9b) illustrates the relative abundance of the two identified isomers of 4502 and the comparison of the different cohorts. 4502_2 with α2,6-linked sialic acid on both arms shows a higher abundance. The alteration in the overall expression of this N-glycan is mostly attributed to its second isomer featuring α2,6-linked sialic acid. A differential expression was observed for 4502_1 and 4502_2 in Day 1 and control comparison in serum. We observed six isomers of 5602 in the serum and five isomers in the CSF, as shown in Figure S9c,d. The designation was according to previously reported studies. For the CSF, differential expressions were observed in Day 1 and the control and Day 3 and the control of isomer 1 (correlates with isomer 2 in serum). More differential expressions were observed in four of the isomers for the serum. The first two isomers associated with α2,3-linked sialic acids showed significant variation in expression across the groups, while the last isomers associated with α2,6-linked sialic acids also showed significant variation in expression across most of the comparisons in serum. Isomer 3, with a mix of α2,3 and α2,6 linked sialic acid, in serum comparisons, showed a higher abundance but with no statistical significance among the comparisons. These observations indicate that distinct sialic acid linkages may have unique functions in disease progression. Terminal sialic acid is generally linked to galactose residues via α2,3- or α2,6-linkages through specific glycosyltransferases. Variations in these linkage isomers have been demonstrated to correlate with various diseases, such as breast cancer. ,

The biosynthetic pathway of glycosylation indicates that they are cell-type- and site-specific and are modulated by the physiological state of the cell. Hence, the different trends observed in the serum and CSF glycans from this study can be attributed to disruptions in glycan biosynthesis pathways triggered by a severe brain injury. Given the roles of glycans in immune response, the presence of brain-specific glycan structures in our study suggests a potential connection between neuroinflammation and neurodegeneration. Our findings provide insights into the glycosylation changes that occur postinjury, establishing a foundation for further investigation of glycans as potential biomarkers and modulators of neuroinflammation.

Despite these insights, the study has several limitations, including the relatively small sample size indicated by the post hoc power analysis, which may limit the robustness of the findings. A considerable number of glycans exhibited low statistical power (≤20%) at the current sample size across all tested effect sizes, ranging from at least 62 to 83 for CSF and 29 to 67 for serum. For serum samples, the median sample sizes estimated to achieve 80% power were approximately 15, 10, and 10 for Control vs Day 1; 20, 20, and 15 for Control vs Day 3; and 10, 12.5, and 10 for Control vs Day 5 at effect sizes of 0.7, 0.8, and 0.9, respectively. For CSF, the corresponding median sample sizes were approximately 25, 22.5, and 20 for Control vs Day 1; 15, 20, and 15 for Control vs Day 3; and 30, 30, and 25 for Control vs Day 5, respectively (Supplementary Table S5). These results reinforce the exploratory nature of this pilot study and provide guidance for future validation efforts.

Expanding this research to a larger cohort will not only enhance statistical power and facilitate biomarker validation but also enable a more detailed assessment of subject heterogeneity, including factors such as age, sex, and sampling variability, to better account for their influence on glycan alterations. Furthermore, while differential glycosylation patterns were observed, direct correlations with clinical outcomes, such as neurological recovery or survival, were not established. Integrating glycomic data with detailed clinical metrics in future studies will provide deeper insight and strengthen biomarker evaluation. Lastly, there was incomplete structural characterization of some isomers. The utilization of standard glycans and complementary approaches, such as glycoproteomics, will enable more comprehensive structural elucidation.

Conclusion

This pilot study provides the first comprehensive glycomic profiling of serum and CSF following severe TBI, uncovering substantial alterations in both N-glycan expression and isomer distribution. Significant findings include distinct systemic (serum) and CNS-specific (CSF) glycosylation responses to injury, opposing trends in fucosylation and sialylation across biofluids, identification of promising glycan as biomarker candidates, and, ultimately, insight into isomer-specific alterations underlying sTBI pathology. Our data provide evidence of the diagnostic potential of glycan-based biomarkers and suggest that glycosylation changes could play critical roles in TBI pathophysiology.

Supplementary Material

ao5c10061_si_001.pdf (2.7MB, pdf)

Acknowledgments

The authors thank members of Mechref Omics Lab and Dr. Puccio’s Lab for helpful discussion.

The raw data is available on GlycoPost with accession number: GPST000548.

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsomega.5c10061.

  • Unsupervised PCA with 95% confidence showing all identified N-glycans; PCA loading plots showing the top 15 N-glycans contributing most to group separation across serum and CSF;bar graphs depicting the sialylated and fucosylated glycan types based on the number of sialic acid or fucose units residues; representative core fucosylated N-glycan structures, showing their EIC, full MS, and MS/MS spectra; EIC of the tetraantennary complex N-glycan 8600, showing its full MS and MS/MS spectra; boxplots showing some statistically significant N-glycans in serum and CSF comparison; correlation heatmap showing the relationship between serum and CSF significant N-glycans; Venn diagram comparing the identified glycans using C18 and MGC LC–MS/MS; representative extracted ion chromatograph and bar graphs of 4502 and 5602 isomers; relative abundance of the identified N-glycans in the serum; relative abundance of the identified N-glycans in the CSF; relative abundance of the glycan isomers identified in the serum; relative abundance of the glycan isomers identified in the CSF; and individual glycan-level post hoc power analysis results for serum and CSF comparisons (PDF)

Conceptualization, Yehia Mechref, Ava M. Puccio, and Joy Solomon; Project administration, supervision, and funding acquisition, Yehia Mechref; Sample design and acquisition, Ava M. Puccio, Stefania Mondello, and Firas H. Kobeissy; Methodology and formal analysis, Joy Solomon, Sherifdeen Onigbinde, Adeniyi Moyinoluwa, Cristian D. Gutierrez-Reyes, Mojibola Fowowe, Md Mostofa Al Amin Bhuiyan, and Judith Nwaiwu; Data curation, Joy Solomon, Sherifdeen Onigbinde, Oluwatosin Daramola, and Adeniyi Moyinoluwa; Visualization, Joy Solomon and Oluwatosin Daramola; Writing original draft preparation, Joy Solomon, Sherifdeen Onigbinde, and Adeniyi Moyinoluwa; Writing review and editing, Joy Solomon, Sherifdeen Onigbinde, Adeniyi Moyinoluwa, Cristian D. Gutierrez-Reyes, Mojibola Fowowe, Md Mostofa Al Amin Bhuiyan, Judith Nwaiwu, Firas H. Kobeissy, Stefania Mondello, Ava M. Puccio, and Yehia Mechref. All authors have approved the final version of the manuscript.

This work was supported by grants from the National Institutes of Health (1R01GM130091- 06, 1R01GM112490- 10), the Robert A. Welch Foundation (No. D-0005), and The CH Foundation.

The authors declare no competing financial interest.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

ao5c10061_si_001.pdf (2.7MB, pdf)

Data Availability Statement

The raw data is available on GlycoPost with accession number: GPST000548.

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