J-Edited DIffusional Proton Nuclear Magnetic Resonance Spectroscopic Measurement of Glycoprotein and Supramolecular Phospholipid Biomarkers of Inflammation in Human Serum.
Philipp NitschkeSamantha LodgeTorben KimhoferReika MasudaSze-How BongDrew HallHartmut SchäferManfred SpraulNils PompeTammo DiercksGaneko Bernardo-SeisdedosJosé M MatoÓscar MilletDaniella SusicAmanda HenryEmad M El-OmarElaine HolmesJohn C LindonJeremy K NicholsonJulien WistPublished in: Analytical chemistry (2022)
Proton nuclear magnetic resonance (NMR) N -acetyl signals (Glyc) from glycoproteins and supramolecular phospholipids composite peak (SPC) from phospholipid quaternary nitrogen methyls in subcompartments of lipoprotein particles) can give important systemic metabolic information, but their absolute quantification is compromised by overlap with interfering resonances from lipoprotein lipids themselves. We present a J-Edited DIffusional (JEDI) proton NMR spectroscopic approach to selectively augment signals from the inflammatory marker peaks Glyc and SPCs in blood serum NMR spectra, which enables direct integration of peaks associated with molecules found in specific compartments. We explore a range of pulse sequences that allow editing based on peak J-modulation, translational diffusion, and T 2 relaxation time and validate them for untreated blood serum samples from SARS-CoV-2 infected patients ( n = 116) as well as samples from healthy controls and pregnant women with physiological inflammation and hyperlipidemia ( n = 631). The data show that JEDI is an improved approach to selectively investigate inflammatory signals in serum and may have widespread diagnostic applicability to disease states associated with systemic inflammation.
Keyphrases
- magnetic resonance
- crispr cas
- oxidative stress
- sars cov
- fatty acid
- molecular docking
- high resolution
- contrast enhanced
- solid state
- blood pressure
- magnetic resonance imaging
- metabolic syndrome
- machine learning
- high fat diet
- healthcare
- electronic health record
- electron transfer
- big data
- social media
- deep learning
- water soluble
- insulin resistance
- data analysis
- artificial intelligence