= Emerging. More than 5 years before clinical availability. (26.62%)
= Expected to be clinically available in 1 to 4 years. (38.91%)
= Clinically available now. (34.47%)
MSACL 2020 US : Torta

MSACL 2020 US Abstract

Topic: Lipidomics

Podium Presentation in Room 2 on Wednesday at 9:40 (Chair: Melanie Odenkirk)

Shared Reference Materials Harmonize Lipidomics in Human Plasma Across MS-based Detection Platforms and Laboratories

Federico Torta (Presenter)
National University of Singapore

Presenter Bio(s): Federico Torta received a PhD in Molecular Biology and Pathology from the University of Parma, Italy, where he studied protein flexibility using hydrogen/deuterium exchange mass spectrometry. As a postdoc he worked in the proteomics field, first at the University of Southern Denmark in Odense and then at San Raffaele Institute in Milan. Today he is a research Assistant Professor at SLING (Singapore Lipidomics Incubator), National University of Singapore, where he focuses on lipid biochemistry for both basic and clinical research.

Authors: Alexander Triebl (1), Bo Burla (1), Jayashree Selvalatchmanan (1), Jeongah Oh (1), Sock Hwee Tan (1), Mark Y. Chan (1), Natalie A. Mellet (7), Peter J. Meikle (7), Federico Torta (1), Markus R. Wenk (1)
(1) National University of Singapore, Singapore (2) Baker Heart and Diabetes Institute, Melbourne, Australia


INTRODUCTION: Quantitative MS of human plasma lipids is a promising technology for translation into clinical applications. Current MS-based lipidomic methods rely on either direct infusion or chromatographic lipid separation methods (including reversed-phase and hydrophilic interaction liquid chromatography). However, the use of lipid markers in laboratory medicine is limited by the lack of reference values, largely because of considerable differences in the concentrations measured by different laboratories worldwide. These inconsistencies can be explained by the use of different sample preparation protocols, method-specific calibration procedures, and other experimental and data-reporting parameters—even when using identical starting materials.

OBJECTIVES: To demonstrate how a method-dependent quantitative bias can be overcome by normalizing to a standard reference material.

METHODS: We systematically investigated the roles of some of these variables in multiple approaches to lipid analysis of plasma samples from healthy adults, considering (1) different sample introduction methods (separation vs. direct infusion methods), (2) different MS instruments and (3) between-laboratory differences in comparable analytical platforms.

RESULTS: Experimental variables resulted in different quantitative results, even with the inclusion of isotope-labelled internal standards for individual lipid classes. We demonstrate that appropriate normalization to commonly available reference samples (i.e., “shared references”) can largely correct for these systematic, method-specific quantitative biases.

CONCLUSION: To harmonize data in the field of lipidomics, in-house, long-term references should be complemented by a commonly available shared reference sample, such as NIST SRM 1950 in the case of human plasma.

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