A comparative evaluation of labeling agents and dopants to enhance low-input N- and O-glycan detection for nanoLC-RP-ESI-MS/MS
Microchemical Journal, cilt.228, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 228
- Basım Tarihi: 2026
- Doi Numarası: 10.1016/j.microc.2026.118992
- Dergi Adı: Microchemical Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, Chemical Abstracts Core, Chimica, Index Islamicus, Academic Search Ultimate (EBSCO), Engineering Source (EBSCO)
- Anahtar Kelimeler: Dopant-enriched nano-ESI, Fluorescent labeling, Glycan profiling, NanoLC-MS/MS, Sensitivity enhancement
- Hacettepe Üniversitesi Adresli: Evet
Özet
Sensitive detection of glycans in limited biological samples remains a significant challenge due of their low abundance and relatively poor ionization efficiency in mass spectrometry. In this study, we systematically evaluated the combined effects of fluorescent labeling reagents and dopant-enriched nano-electrospray ionization to improve the detection sensitivity of N- and O-glycans using a nanoLC-RP-ESI-MS/MS platform. Fetuin was used as a model glycoprotein to compare three labeling agents (2-aminobenzoic acid (2-AA), 2-aminobenzamide (2-AB), and procainamide (Proc)) and three dopant solvents (acetonitrile, methanol, and isopropanol). Among the tested combinations, 2-AA labeling coupled with methanol-doped ionization produced the highest signal intensity for N-glycans, whereas the 2-AB + methanol combination produced the strongest response for O-glycans. Chromatographic optimization further revealed that trifluoroacetic acid improved N-glycan separation, whereas formic acid yielded better peak shapes for O-glycans. The tandem MS evaluation indicated that basic stepping CID provided the most comprehensive fragmentation coverage and the highest identification confidence. The application of the optimized workflow to HT29 colorectal cancer cells enabled the identification of 21 N-glycan structures, with reliable glycan detection achievable from as few as 105 cells. These findings establish an optimized nanoLC-MS strategy for sensitive glycomic profiling of low-input biological samples.