The status of metabolomics as a scientific branch has evolved from proof-of-concept to applications in science, particularly in medical research. To comprehensively evaluate disease metabolomics, multiplatform approaches of NMR combining with mass spectrometry (MS) have been investigated and reported. This mixed-methods approach allows for the exploitation of each individual technique’s unique advantages to maximize results. In this article, we present our findings from combined NMR and MS imaging (MSI) analysis of human lung and prostate cancers. We further provide critical discussions of the current status of NMR and MS combined human prostate and lung cancer metabolomics studies to emphasize the enhanced metabolomics ability of the multiplatform approach.

There is currently a crucial need for improved diagnostic techniques and targeted treatment methods for Alzheimer’s disease (AD), a disease which impacts millions of elderly individuals each year. Metabolomic analysis has been proposed as a potential methodology to better investigate and understand the progression of this disease. In this report, we present our AD metabolomics results measured with high resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) on human blood plasma samples obtained from AD and non-AD subjects. Our study centers on developments of AD and non-AD metabolomics differentiating models with procedures of quality assurance (QA) and quality control (QC) through pooled samples. Our findings suggest that analysis of blood plasma samples using HRMAS NMR has the potential to differentiate between diseased and healthy subjects, which has important clinical implications for future improvements in AD diagnosis methodologies.

Unfortunately, at present, there is no single technique that possesses all the characteristics needed to be considered an ideal global metabolite profiling tool. Thus, the use of multiple analytical platforms, such as combining the strengths of Mass spectrometry (MS) and nuclear magnetic resonance spectroscopy (NMR), for metabolic profiling can maximize coverage and generate more global metabolomic profiles. In this study, we demonstrate the utilities of the combined NMR and MSI multiplatform in our metabolomics results on human prostate and lung cancers. Statistical data on the natural history of prostate cancer (PCa) show that >70% of patients diagnosed by PSA screening will likely experience indolent disease with little impact on well-being. For about 17% of newly PSA-diagnosed patients, however, aggressive PCa proliferation ensues, truncating life expectancy. At present, no reliable clinical test can differentiate between these two groups. Using HRMAS 1HNMR followed by quantitative histology, we showed statistically significant correlations between concentrations of Spm and the amount of histologically-benign epithelial (Hb Epi) prostatic cells and glands in human cancerous prostates. However, as above discussed that using HRMAS NMR alone we cannot prove that Spm was indeed generated or resided in the Hb Epi cells. Nevertheless, using MALDI MSI, we were able to locate Spm (m/z: 203.223 ± 0.001Da) onto Hb Epi, where spermine on the PCa lesions appeared below detection limits. From these maps, for the first time, we could visualize and confirm the differential localizations of Spm in prostates. This proof of Sym relationship to prostate pathologies and its proposed PCa inhibitory effects may support further studies that are critical in differentiating aggressive from indolent PCa for disease evaluations and patient personalized treatment strategies. To search for such screening metabolomics biomarkers in lung cancer, we used HRMAS NMR to analyze 93 pairs of human LuCa tissue and serum samples, and 29 healthy human sera. A number of potential metabolite candidates capable to differentiate LuCa characteristics were identified, including glutamate, lipids, alanine, glycerylphosphorylcholine, glutamine, phosphorylcholine, etc. This list can be further expanded by analyzing metabolite composition in the serum of cancer patients and control healthy subjects using LC-MS, which offers a dramatic increase in sensitivity compared to HRMAS NMR and, therefore, is better suited for the biomarker discovery. In addition to acquiring high-resolution mass data, the high data acquisition rate allows the fragment ion mass spectra (MS/MS) to be generated for the most abundant ionic species in each chromatographic peak. This feature allows specific classes of tumor-attenuated metabolites to be identified based on the presence of unique structurally diagnostic fragment ions in MS/MS spectra. Citation Format: Leo L. Cheng, Anya B. Zhong, Isabella H. Muti, Stephen J. Eyles, Richard W. Vachet, Sylwia A. Stopka, Kristen N. Sikora, Cedric E. Bobst, Jeffrey N. Agar, Mari A. Mino-Kenudson, Chin-Lee Wu, David C. Christiani, Igor A. Kaltashov, Nathalie Y. Agar. Multiplatform metabolomics studies of human cancers with NMR and mass spectrometry imaging [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 2322.