Strategies Mika Tiainen Quantitative Quantum Mechanical

Nuclear magnetic resonance (NMR) spectroscopy is widely used for profiling of a variety of complex biological samples. The advantages of NMR are its quantitative and nondestructive nature, high reproducibility and that measurements can be done with minimal sample preparation and instrument calibration. However, accurate quantification of compounds from a 1H NMR spectrum of biological sample, where the number and concentration range of compounds is large, is a demanding task due to spectral complexity and overlap. Further complications may arise from any instrumental or experimental artefacts. Additionally, the sample conditions, mainly pH and ionic strength, may cause variations to the chemical shifts and line widths. A distinctive feature of high-resolution 1D NMR spectra is that even the most complex spectrum of a compound, composed of thousands of individual spectral lines, can be described by a few spectral parameters within experimental accuracy employing a quantum mechanical (QM) model. Thus, even in the case of the 1H NMR spectrum of a complex mixture, there are strict QM rules between the lines of individual compounds. The spectral parameters can be extracted from the observed spectra by quantum mechanical spectral analyses (QMSA). In the present thesis, quantification methods, strategies and protocols for NMR spectra of different biological samples were developed. Also adaptive spectral library (ASL) principle including 1H NMR pH indicators was developed and discussed. ASL can be described as a library of spectral parameters obtained through QMSA. The parameters in the library can be used to simulate the spectra of the compounds in any magnetic field, line shape, line widths and, also, taking into account different sample conditions like pH or solvent. Thus, these parameters can be used as a starting point of quantitative Quantum Mechanical Spectral analysis (qQMSA), which means the complete iterative analysis of the spectra based on the QM spectral model and offers an ideal tool for quantification of complex 1H NMR spectra. qQMSA including models describing unknown components, background and prior knowledge from the sample enables modelling of even the smallest details of the spectrum and the maximal quantitative NMR information analysis. In addition to accurate concentrations of known metabolites, qQMSA offers chemical confidence, which means that individual components are identified with high confidence on the basis of their spectral parameters. Also, a protocol for quantification of amino acid 13C isotopomers and determination of positional fractional 13C enrichments for metabolic 13C tracer experiments was developed. This application offers an extreme example of ASL. The protocols and tools developed in this study, ASL and qQMSA, enable accurate, robust and cost-effective way to quantify individual components from the NMR spectra of complex mixtures. National Library of Medicine Classification: QU 25, QU 60, QV 25, QV 744 Medical Subject Headings: Chemistry Techniques, Analytical; Spectrum Analysis; Magnetic Resonance Spectroscopy; Nuclear Magnetic Resonance, Biomolecular; Metabolomics; Amino Acids/analysis; Glucose/analysis; Hydrogen-Ion Concentration