Development and Application of Ultra-Performance Liquid Chromatography-TOF MS for Precision Large Scale Urinary Metabolic Phenotyping

Abstract

To better understand the molecular mechanisms underpinning physiological variation in human populations, metabolic phenotyping approaches are increasingly being applied to studies involving hundreds and thousands of biofluid samples. Hyphenated ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) has become a fundamental tool for this purpose. However, the seemingly inevitable need to analyze large studies in multiple analytical batches for UPLC-MS analysis poses a challenge to data quality which has been recognized in the field. Herein, we describe in detail a fit-for-purpose UPLC-MS platform, method set, and sample analysis workflow, capable of sustained analysis on an industrial scale and allowing batch-free operation for large studies. Using complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) together with high resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS), exceptional measurement precision is exemplified with independent epidemiological sample sets of approximately 650 and 1000 participant samples. Evaluation of molecular reference targets in repeated injections of pooled quality control (QC) samples distributed throughout each experiment demonstrates a mean retention time relative standard deviation (RSD) of <0.3% across all assays in both studies and a mean peak area RSD of <15% in the raw data. To more globally assess the quality of the profiling data, untargeted feature extraction was performed followed by data filtration according to feature intensity response to QC sample dilution. Analysis of the remaining features within the repeated QC sample measurements demonstrated median peak area RSD values of <20% for the RPC assays and <25% for the HILIC assays. These values represent the quality of the raw data, as no normalization or feature-specific intensity correction was applied. While the data in each experiment was acquired in a single continuous batch, instances of minor time-dependent intensity drift were observed, highlighting the utility of data correction techniques despite reducing the dependency on them for generating high quality data. These results demonstrate that the platform and methodology presented herein is fit-for-use in large scale metabolic phenotyping studies, challenging the assertion that such screening is inherently limited by batch effects. Details of the pipeline used to generate high quality raw data and mitigate the need for batch correction are provided.

The effects of kisspeptin on β‐cell function, serum metabolites and appetite in humans

“…Serum metabolite identification and analysesBlood samples for metabolic profiling were taken pre-infusion (T = −15 minutes) and at steady state prior to intravenous glucose or meal ingestion (T = 45 minutes). Serum sample handling (sorting and formatting) was performed as previously described,26 and 50 μL aliquots were taken from each sample for metabolite analyses. Protein was removed from the samples prior to analysis by addition of organic solvent, mixing and centrifugation, yielding a homogenous supernatant (method-specific details are included in the supporting information for this article).…”

“…The prepared samples were subjected to small molecule and lipid analyses by ultra-performance liquid chromatography mass spectrometry (UPLC-MS). Reversed-phase chromatography tailored for complex lipid retention and separation was used to profile the lipid species of each sample, while hydrophilic interaction liquid chromatography (HILIC) was used to retain and separate polar metabolites 26. High resolution orthogonal acceleration time-of-flight mass spectrometry (oaTOF-MS) operating in the positive ion mode was used for both assays.…”

Data Streaming for Metabolomics: Accelerating Data Processing and Analysis from Days to Minutes

“…11 (A) A Large time savings are gained by “Online Streaming” with results being generated only 4 h after data acquisition compared to “Manual Uploading” (18h). Data dead time is the time after the completion of data acquisition and data uploading for processing.…”

“…, where 1000 urine samples were analyzed using LC-MS (Figure 4B). 11 In this modality, in contrast to online streaming, data files are streamed to XO Cloud after all data files have been acquired. These files can be located at computers both on the instrument or personal computers used by users for data analysis.…”

NMR-based pharmacometabonomics: A new paradigm for personalised or precision medicine

“…Each of these steps has been well investigated for either NMR- [14][15][16][17][18][19][20] or mass spectrometry (MS)-based [21][22][23][24] approaches to metabolic profiling. The step that presents the most difficulties to investigators using either NMR-based or MS-based approaches is metabolite identification (see Section 2.4 below).…”