Multi-platform NMR Study of Pluripotent Stem Cells Unveils Complementary Metabolic Signatures towards Differentiation.

Stem cells, poised to revolutionize current medicine, stand as major workhorses for monitoring changes in cell fate. Characterizing metabolic phenotypes is key to monitor in differentiating cells transcriptional and epigenetic shifts at a functional level and provides a non-genetic means to control cell specification. Expanding the arsenal of analytical tools for metabolic profiling of cell differentiation is therefore of importance.

Placental physiology monitored by hyperpolarized dynamic C magnetic resonance.

Placental functions, including transport and metabolism, play essential roles in pregnancy. This study assesses such processes in vivo from a hyperpolarized MRI perspective. Hyperpolarized urea, bicarbonate, and pyruvate were administered to near-term pregnant rats, and all metabolites displayed distinctive behaviors.

Following Metabolism in Living Microorganisms by Hyperpolarized (1)H NMR.

Dissolution dynamic nuclear polarization (dDNP) is used to enhance the sensitivity of nuclear magnetic resonance (NMR), enabling monitoring of metabolism and specific enzymatic reactions in vivo. dDNP involves rapid sample dissolution and transfer to a spectrometer/scanner for subsequent signal detection. So far, most biologically oriented dDNP studies have relied on hyperpolarizing long-lived nuclear spin species such as (13)C in small molecules.

Metabolomic profiles of hepatocellular carcinoma in a European prospective cohort.

Background: Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is difficult to diagnose and has limited treatment options with a low survival rate. Aside from a few key risk factors, such as hepatitis, high alcohol consumption, smoking, obesity, and diabetes, there is incomplete etiologic understanding of the disease and little progress in identification of early risk biomarkers.

Methods: To address these aspects, an untargeted nuclear magnetic resonance metabolomic approach was applied to pre-diagnostic serum samples obtained from first incident, primary HCC cases (n = 114) and matched controls (n = 222) identified from amongst the participants of a large European prospective cohort.

A statistical framework to model the meeting-in-the-middle principle using metabolomic data: application to hepatocellular carcinoma in the EPIC study.

Metabolomics is a potentially powerful tool for identification of biomarkers associated with lifestyle exposures and risk of various diseases. This is the rationale of the 'meeting-in-the-middle' concept, for which an analytical framework was developed in this study. In a nested case-control study on hepatocellular carcinoma (HCC) within the European Prospective Investigation into Cancer and nutrition (EPIC), serum (1)H nuclear magnetic resonance (NMR) spectra (800 MHz) were acquired for 114 cases and 222 matched controls.

Batch profiling calibration for robust NMR metabonomic data analysis.

Metabonomic studies involve the analysis of large numbers of samples to identify significant changes in the metabolic fingerprints of biological systems, possibly with sufficient statistical power for analysis. While procedures related to sample preparation and spectral data acquisition generally include the use of independent sample batches, these might be sources of systematic variation whose effects should be removed to focus on phenotyping the relevant biological variability. In this work, we describe a grouped-batch profile (GBP) calibration strategy to adjust nuclear magnetic resonance (NMR) metabolomic data-sets for batch effects either introduced during NMR experiments or samples work-up.