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.
Highlights
Article molecular epidemiology and personalized healthcare.[2]
While the analysis of blood products has been the subject of recent advances fit for the purpose of largescale application,[22] the ultra-performance liquid chromatography−mass spectrometry (UPLC-MS) approaches used for analysis of human urine have not been sufficiently demonstrated for this purpose
Complementary reversed-phase chromatography (RPC) and hydrophilic interaction liquid chromatography (HILIC) separations coupled to oaTOF-MS are individually the most common Liquid chromatography coupled to mass spectrometry (LC-MS) techniques used for metabolic phenotyping of urine[31] and were developed here for high precision chromatographic separation within a window of time defined by the practical constraints imposed by a high throughput working laboratory
Summary
Article molecular epidemiology and personalized healthcare.[2]. The field has been propelled by advances in the analytical technology and data processing methods required to capture and interpret data derived from the metabolic pathways of complex biochemical systems.[3,4] Metabolic profiling, unlike conventional clinical chemistry analyses, is not intended to be selective and generates simultaneous measurement of both expected and potentially uncharacterized metabolites, making the approach fruitful in biomarker discovery.[5]. Data sets may be further refined by removal of feature groups that do not meet an arbitrary threshold of peak area measurement precision prior to downstream analysis This approach, utilizing RSD values derived from repeated measurements of a pooled QC sample, is becoming increasingly mainstream in molecular profiling literature.[50] such an approach fails to account for the relationship between the observed analytical and total (including biological) variation in each chemical species measured. Batch correction tools that produce high precision data sets without erroneously constraining meaningful biological variance remain valuable assets
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