Automated On-Line Isolation and Fractionation System for Nanosized Biomacromolecules from Human Plasma.
Evgen MultiaThanaporn LiangsupreeMatti JussilaJose Ruiz-JimenezMarianna L KemellMarja-Liisa RiekkolaPublished in: Analytical chemistry (2020)
An automated on-line isolation and fractionation system including controlling software was developed for selected nanosized biomacromolecules from human plasma by on-line coupled immunoaffinity chromatography-asymmetric flow field-flow fractionation (IAC-AsFlFFF). The on-line system was versatile, only different monoclonal antibodies, anti-apolipoprotein B-100, anti-CD9, or anti-CD61, were immobilized on monolithic disk columns for isolation of lipoproteins and extracellular vesicles (EVs). The platelet-derived CD61-positive EVs and CD9-positive EVs, isolated by IAC, were further fractionated by AsFlFFF to their size-based subpopulations (e.g., exomeres and exosomes) for further analysis. Field-emission scanning electron microscopy elucidated the morphology of the subpopulations, and 20 free amino acids and glucose in EV subpopulations were identified and quantified in the ng/mL range using hydrophilic interaction liquid chromatography-tandem mass spectrometry (HILIC-MS/MS). The study revealed that there were significant differences between EV origin and size-based subpopulations. The on-line coupled IAC-AsFlFFF system was successfully programmed for reliable execution of 10 sequential isolation and fractionation cycles (37-80 min per cycle) with minimal operator involvement, minimal sample losses, and contamination. The relative standard deviations (RSD) between the cycles for human plasma samples were 0.84-6.6%.
Keyphrases
- liquid chromatography tandem mass spectrometry
- ms ms
- electron microscopy
- liquid chromatography
- solid phase extraction
- mass spectrometry
- simultaneous determination
- nk cells
- stem cells
- mesenchymal stem cells
- machine learning
- small cell lung cancer
- blood pressure
- high performance liquid chromatography
- single cell
- tandem mass spectrometry
- deep learning
- health risk
- high resolution mass spectrometry
- drinking water
- type diabetes
- metabolic syndrome
- adipose tissue
- gas chromatography
- climate change
- solid state
- human health