Comparison of Two Automated Targeted Metabolomics Programs to Manual Profiling by an Experienced Spectroscopist for 1H-NMR Spectra

Abstract

Automated programs that carry out targeted metabolite identification and quantification using proton nuclear magnetic resonance spectra can overcome time and cost barriers that limit metabolomics use. However, their performance needs to be comparable to that of an experienced spectroscopist. A previously analyzed pediatric sepsis data set of serum samples was used to compare results generated by the automated programs rDolphin and BATMAN with the results obtained by manual profiling for 58 identified metabolites. Metabolites were selected using Student’s t-tests and evaluated with several performance metrics. The manual profiling results had the highest performance metrics values, especially for sensitivity (76.9%), area under the receiver operating characteristic curve (0.90), precision (62.5%), and testing accuracy based on a neural net (88.6%). All three approaches had high specificity values (77.7–86.7%). Manual profiling by an expert spectroscopist outperformed two open-source automated programs, indicating that further development is needed to achieve acceptable performance levels.

Mapping of 1H NMR chemical shifts relationship with chemical similarities for the acceleration of metabolic profiling: Application on blood products

“…As previously shown, 18 for the metabolites exhibiting only one singlet, the assignment was validated (when needed) via spiking experiments (Figure S1 ) along with already published tools (i.e., SMolESY‐select, 18 Chenomx NMR suite 8.1 [evaluation license] [ www.chenomx.com ]). All assignments were performed by an experienced NMR spectroscopist 27 corroborated by the above software when needed (several examples of spiking experiments as well as assignments are described in Figure S1 ). Figure 1a,b depicts the distribution and z ‐scored ranges of each metabolite δ for both serum and plasma matrices.…”

Insights into Nuclear Magnetic Resonance Data Pre-processing: A Comprehensive Review

“…After the baseline is constructed, it is subtracted from the corresponding spectrum. Most baseline correction methods are automated, although semi-automatic or manual baseline correction methods also exist [29,[70][71][72][73].…”

“…To ensure comparability of NMR spectra across different spectrometers, frequencies are expressed in parts per million (ppm) using the ratio of a signal's frequency to the spectrometer's frequency. Calibration, also known as global alignment, sets the internal reference signal's ppm to zero by shifting the entire spectrum [17,63,70,72,79]. On the other hand, (local) alignment is to align each peak across a group of spectra to the same ppm position [17,68].…”