# Sensitive and Quantitative MS-bases Glycomic Mapping Platform > **NIH NIH R01** · TEXAS TECH UNIVERSITY · 2021 · $316,105 ## Abstract Glycosylation is one of the most complex protein modifications; more than 50% of mammalian proteins are glycosylated. The fact that there are 100,000 proteoforms coded by only ~20,300 genes identified in the human genome emphasizes the importance of posttranslational modifications (PTMs) like glycosylation. Aberrant protein glycosylation has been implicated in many diseases, such as Alzheimer’s disease, congenital/metabolic disorders, diabetes, inflammation, Parkinson’s disease, bacterial/viral infectious diseases, and various cancers. More recently, glycans have been associated with coronavirus spike glycoproteins, including the SARS- CoV-2 virion. The diverse biological roles of glycans and their implications in diseases have created a demand for reliable qualitative and quantitative glycomic approaches, which facilitates sensitive investigation of glycan changes in different biological and biomedical samples. Mass spectrometry (MS) is the most efficient technique in glycomics due to its high sensitivity and capacity for acquiring structural information. However, glycomic research remains a challenge because of the microheterogeneity of glycan compositions in complex biological samples; the relatively low abundance in nature and low ionization efficiency in MS analysis; and the existence of variant positional and linkage isomers caused by the biosynthesis process. To overcome these challenges, several separation methods have been coupled to MS. Despite the development of these separation techniques, isomeric separation of glycans remains insufficient. There is an increasing demand for more efficient isomeric separation approaches since glycan isomers have been related to different diseases. The main aim of this proposal is to provide easily accessible, adaptable, and affordable strategies for better separation and characterization of glycans and glycan isomers derived from different glycoconjugates. Aim 1 is focused on finding a replacement for porous craphitic carbon columns that sufferes from low reprodcubility and loss of resolution and efficiency with time. The in-house mesoporous graphitic carbon (MGC)-LC-MS will be investigated for both permethylated (Aim 1a) and native (Aim 2b) isomeric separation of N- and O-glycans, glycolipid glycans, and free oligosaccharides. Other alternatives (also part of Aim 1a), such as 50 cm and 200 cm micro pillar array columns (μPAC)-C18-LC-MS, and a 50 cm long capillary nanoC18-LC-MS, will also be evaluated to achieve an improved isomeric separation of glycan isomers. Subsequently, GUI will be utilized to improve the identification of glycan isomers. A GUI libraries for the separation strategies developed in Aim 1 will be established to normalize the possible retention time shift among different runs (Aim 2). LC-M based glycomics quantitative strategies will be developed and assessed in Aim 3. The combination of permethylation and TMT will capitalize on the advantages of both techniques, providing enhanced... ## Key facts - **NIH application ID:** 10318016 - **Project number:** 2R01GM112490-08 - **Recipient organization:** TEXAS TECH UNIVERSITY - **Principal Investigator:** Yehia Mechref - **Activity code:** R01 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2021 - **Award amount:** $316,105 - **Award type:** 2 - **Project period:** 2014-09-15 → 2025-08-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10318016 ## Citation > US National Institutes of Health, RePORTER application 10318016, Sensitive and Quantitative MS-bases Glycomic Mapping Platform (2R01GM112490-08). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10318016. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*