Single Molecule Identification and Quantification of Glycosaminoglycans Using Solid-State Nanopores

Area of Science:

• Biochemistry
• Analytical Chemistry
• Biophysics

Background:

• Glycosaminoglycans (GAGs) are complex polysaccharides with significant biological roles.
• Current analytical methods face challenges in GAG analysis due to heterogeneity and structural complexity.

Purpose of the Study:

• To develop and validate a novel analytical technique for GAG quantification and identification.
• To address the limitations of existing methods for analyzing complex GAG mixtures.

Main Methods:

• Integration of solid-state nanopores (single-molecule sensors) with a Support Vector Machine (SVM) machine learning algorithm.
• Utilizing the nanopore sensor to detect and differentiate GAG fragments based on their unique translocation characteristics.

Main Results:

• The nanopore/SVM technique achieved >90% accuracy in distinguishing heparin from chondroitin sulfate fragments.
• Detection of as low as 0.8% (w/w) chondroitin sulfate impurities in heparin samples.
• Distinguished between unfractionated heparin (UFH) and enoxaparin (low molecular weight heparin) with ~94% accuracy.
• Demonstrated nanomolar sensitivity and a 5-Log dynamic range with nanopore calibration.
• Accurate quantification of heparin using multiple nanopores.

Conclusions:

• The nanopore/SVM technique offers a highly accurate and sensitive method for GAG analysis.
• This approach has the potential to significantly advance the quality control and characterization of GAG-based therapeutics.
• The method provides a powerful tool for identifying and quantifying complex GAG mixtures.