An algorithm for the automated quantitation of metabolites in in vitro NMR signals

Abstract

The quantitation of metabolite concentrations from in vitro NMR spectra is hampered by the sensitivity of peak positions to experimental conditions. The quantitation methods currently available are generally labor intensive and cannot readily be automated. Here, an algorithm is presented for the automatic time domain analysis of high-resolution NMR spectra. The TARQUIN algorithm uses a set of basis functions obtained by quantum mechanical simulation using predetermined parameters. Each basis function is optimized by subdividing it into a set of signals from magnetically equivalent spins and varying the simulated chemical shifts of each of these groups to match the signal undergoing analysis. A novel approach to the standard multidimensional minimization problem is introduced based on evaluating the fit resulting from different permutations of possible chemical shifts, obtained from one-dimensional searches. Results are presented from the analysis of (1)H proton magic angle spinning spectra of cell lines illustrating the robustness of the method in a typical application. Simulation was used to investigate the biggest peak shifts that can be tolerated.

Non-invasive detection of glycine as a biomarker of malignancy in childhood brain tumours using in-vivo 1 H MRS at 1.5 Tesla confirmed by ex-vivo high-resolution magic-angle spinning NMR

“…In the group for which both short-TE and long-TE spectra were obtained, agreement between the Gly concentrations estimated at each TE was investigated by determining the Pearson's correlation coefficient. For the group with HRMAS data, spectra were fitted using the TARQUIN algorithm (27) and the Gly and mI concentrations normalised by the sum of all metabolite concentrations were compared between low grade and high grade tumours and correlated with the in-vivo results.…”

A comparison between simulated and experimental basis sets for assessing short‐TE in vivo1H MRS data at 1.5 T

“…This confirms that simulated basis sets can be used rather than experimental ones, offering the advantage that they can be easily generated for different localisation sequences and TEs once the metabolite parameters have been determined. A further advantage of simulated basis sets is that metabolites with multiple signal groups can be defined and adjusted independently, an approach which is particularly interesting for high-field studies, where it may be important to account for peak shifting (35).…”

Quantification Improvements of 1H MRS Signals

“…• Time-and frequency domain fitting using a linear combination of individual peaks/profiles to fit the spectra QUEST (Ratiney et al, 2004), AQSES (Poullet et al, 2007) LCModel (Provencher, 1993;2001) • Time-domain estimation of parameters using prior knowledge Young et al, 1998), AMARES • Time-domain non-iterative fitting methods such as HLSVD (Barkhuijsen et al, 1987;Chen et al, 1996;Dologlou et al, 1998;Laudadio et al, 2002;Pijnappel et al, 1992;van den Boogaart, 1997) • Iterative time-and frequency domain fitting (Slotboom et al, 1998) • Semi-parametric fitting (Elster et al, 2005) • Time-domain variable projection (VARPRO) (Cavassila et al, 1999;van der Veen et al, 1988) • Time domain fitting of one peak at a time and wavelet modeling for the baseline (Dong et al, 2006;Romano et al, 2002) • Constrained least squares (TARQUIN) (Reynolds et al, 2006;Wilson et al, 2011) • Genetic algorithms (Metzger et al, 1996) • Fast Padé Transform (Belkić&Belkić, 2006) • Artificial Neural Networks (Bhat et al, 2006;Hiltunen et al, 2002) • Sparse representation (Guo et al, 2010) • Circular fitting (Gabr et al, 2006) • Principal Component Analysis (PCA), Independent Component Analysis (ICA) (Hao et al, 2009;Stoyanova & Brown, 2001) …”