Candice Zena Ulmer

Metabolomics encompasses the comprehensive study of metabolism as it pertains to a biological system in response to a stimulus or pathophysiological condition. Lipidomics, a subset discipline of metabolomics, encompasses the study of lipid pathways, networks, functions, and interactions. While the term “metabolome” was coined in 1998 and the metabolomics field was considered emerging until recently, metabolic profiles have been studied in biological fluids for centuries. Metabolomics methodologies are currently divided into 2 main categories: targeted and untargeted studies. Targeted metabolomics studies apply single to multianalyte quantitative approaches to analyze a predefined selection (1 to 100s) of compounds originating from a specific metabolic pathway. Untargeted metabolomics studies apply chemometric approaches to focus on the comprehensive or global analysis of all detectable metabolites in a biological sample in a hypothesis-generating manner. The goal of targeted and untargeted metabolomics is to use the metabolic profile to infer biological functions within a system and better understand biochemical responses. In addition, the workflows for both approaches include bioanalysis (e.g., sample collection, sample preparation, and data acquisition), data processing, compound identification, and biological interpretation.

Currently the 25-hydroxy vitamin D (25(OH)D) concentration is thought to be the best estimate of the vitamin D status of an individual. Unfortunately, its measurement remains complex, despite recent technological advances. We evaluated the biological variation (BV) of 25(OH)D in order to set analytical performance specifications (APS) for measurement uncertainty (MU). Six European laboratories recruited 91 healthy participants. The 25(OH)D concentrations in K₃-EDTA plasma were examined weekly for up to 10 weeks in duplicate on a Lumipulse G1200 (Fujirebio, Tokyo, Japan). The linear regression of the mean 25(OH)D concentrations at each blood collection showed that participants were not in a steady state. The dissection of the 10-sample collection into two subsets, namely collections 1–5 and 6–10, did not allow for correction of the lack of homogeneity: estimates of the within-subject BV ranged from 5.8% to 7.1% and the between-subject BV ranged from 25.0% to 39.2%. Methods that would differentiate a difference induced by 25(OH)D supplementation at p < 0.05 should have MU < 13.6%, while at p < 0.01, the MU should be <9.6%. The development of APS using BV assumes a steady state of patients. The findings in this study suggest that patients are not in steady state. Therefore, APS that are based on MU appear to be more appropriate.

A sensitive, selective, and quantitative method incorporating high-resolution mass spectrometry was developed for the determination of blood urea nitrogen (BUN) in bronchoalveolar lavage fluid. The method requires no sample clean-up or derivatization prior to analysis. High performance liquid chromatography (HPLC) on a Hypersil Gold PFP column (100 x 3 mm, 3 µm particle size) connected to a C18 guard column was employed for a 10-minute chromatographic separation. The detection of urea was achieved using a Thermo Scientific Q-Exactive Plus incorporating selected ion monitoring (SIM) modes for the protonated adduct of urea. The urea analytical measuring range for the method is 0.047 – 17.134 mg/dL, resulting in a BUN analytical measurement range of 0.022 – 8.007 mg/dL, which allows for quantitation over three orders of magnitude (R² = 0.999). In addition, the method is suitable for small sample volumes (15 µL) with a high level of accuracy, precision, and specificity.

Lipidomics, the comprehensive measurement of lipids within a biological system or substrate, is an emerging field with significant potential for improving clinical diagnosis and our understanding of health and disease. While lipids diverse biological roles contribute to their clinical utility, the diversity of lipid structure and concentrations prove to make lipidomics analytically challenging. Without internal standards to match each lipid species, researchers often apply individual internal standards to a broad range of related lipids. To aid in standardizing and automating this relative quantitation process, we developed LipidMatch Normalizer (LMN) http://secim.ufl.edu/secim-tools/ which can be used in most open source lipidomics workflows. LMN uses a ranking system (1–3) to assign lipid standards to target analytes. A ranking of 1 signifies that both the lipid class and adduct of the internal standard and target analyte match, while a ranking of 3 signifies that neither the adduct or class match. If multiple internal standards are provided for a lipid class, standards with the closest retention time to the target analyte will be chosen. The user can also signify which lipid classes an internal standard represents, for example indicating that ether-linked phosphatidylcholine can be semi-quantified using phosphatidylcholine. LMN is designed to work with any lipid identification software and feature finding software, and in this study is used to quantify lipids in NIST SRM 1950 human plasma annotated using LipidMatch and MZmine. LMN can be integrated into an open source workflow which completes all data processing steps including feature finding, annotation, and quantification for LC-MS/MS studies. Using LMN we determined that in certain cases the use of peak height versus peak area, certain adducts, and negative versus positive polarity data can have major effects on the final concentration obtained.