Differences in levels of phosphatidylinositols in healthy and stable Coronary Artery Disease subjects revealed by HILIC-MRM method with SERRF normalization.

Yue Huang,Joseph S Grimsby,Meina Liang,Ruipeng Mu,Anton I Rosenbaum,David Wen,Jon M Jacobs

doi:10.1371/journal.pone.0252426

Yue Huang, Joseph S Grimsby + Show 5 more

Open Access

https://doi.org/10.1371/journal.pone.0252426

Copy DOI

Journal: PloS one	Publication Date: Jun 4, 2021
Citations: 5	License type: CC BY 4.0

Affiliation: AstraZeneca (United States)

Abstract

Quantification of endogenous biomarkers in clinical studies requires careful evaluation of a number of assay performance parameters. Comparisons of absolute values from several clinical studies can enable retrospective analyses further elucidating the biology of a given biomarker across various study populations. We characterized the performance of a highly multiplex bioanalytical method for quantification of phosphatidylinositols (PI). Hydrophilic interaction chromatography (HILIC) and multiple reaction monitoring (MRM) were employed for targeted multiplex quantification. Odd-chain PI species that are not normally present in human plasma were utilized as surrogate analytes (SA) to assess various assay performance parameters and establish a definitive dynamic linear range for PI lipids. To correct for batch effects, Systematic Error Removal using Random Forest (SERRF) normalization algorithm was employed and used to bridge raw values between two clinical studies, enabling quantitative comparison of their absolute values. A high throughput method was developed, qualified, transferred to an automation platform and applied to sample testing in two clinical trials in healthy volunteers (NCT03001297) and stable Coronary Artery Disease (CAD, NCT03351738) subjects. The method demonstrated acceptable precision and accuracy (±30%) over linear range of 1–1000 nM for SA and 8-fold dilutional linearity for endogenous PI. We determined that mean-adjusted average QC performed best for normalization using SERRF. The comparison of two studies revealed that healthy subject levels of PI are consistently higher across PI species compared to CAD subjects identifying a potential lipid biomarker to be explored in future studies.

Highlights

Lipids are a major component of plasma [1]
In order to correct for batch effects observed during clinical sample analysis we examined the performance of the recently published Systematic Error Removal using Random Forest (SERRF) algorithm [36] for normalization of batch-to-batch variability across the two studies
With lipidomic approaches aiming at full lipid profiling, proper quantitative characterization of lower abundance lipids for use as clinical biomarkers is an area of emerging interest that requires further inquiry

Summary

Introduction

Lipids are a major component of plasma [1]. The concentration and profile of plasma lipids are related to the individual’s diet [2] and system metabolism that reflects multiple aspects of the individual’s health status [3, 4]. Careful characterization of the quantitative properties of a high throughput bioanalytical method for the measurement of human plasma PI is valuable. Direct infusion has the benefits of fast sample analysis and high throughput [25, 26]. The chromatographic separation methods usually have lower throughput but may provide additional specificity and selectivity. The common chromatographic separation methods used for quantitative lipid analysis include normal phase liquid chromatography (NPLC) [22], reverse phase liquid chromatography (RPLC) [27] and hydrophilic interaction liquid chromatography (HILIC) [28,29,30] as well as multi-dimensional separation [31, 32].

Methods

Results

Conclusion