Quality Assurance and Quality Control in LC-MS-based Targeted Lipidomics of Human Blood Plasma
Bo Burla (1), Jeremy John Selva (2), Shanshan Ji (1), , Peter Benke (2), Anne K. Bendt (1), Federico Torta (2), Markus R. Wenk (1,2) (1) Singapore Lipidomics Incubator (SLING), Life Sciences Institute and (2) Department of Biochemistry, YLL School of Medicine, National University of Singapore, Singapore
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Bo Burla (Presenter) National University of Singapore
Presenter Bio: At SLING in Singapore for more than two years, I am working at the interface between the analytics in the lab and our data team to explore and implement new data processing workflows and pipelines for our lipidomics platform.
Relevant Financial Disclosures
(within past 24 months)
No relevant financial relationship(s) to disclose.
Abstract
Ensuring quality and reproducibility of large-scale, large-panel LC-MS-based lipidomics analyses is critical but still challenging. Experimental design, sample processing, matrix effects, instrumental variations and data processing can all introduce variations and inter- and intra-assay batch effects, which can bias or cofound experimental readouts. We here explore frequently used and novel quality assurance (QA) and quality control (QC) strategies based on internal standards, different QC sample types, reference materials and different corresponding data processing methods and software tools. We demonstrate these strategies with analyses of large-scale studies and specifically designed tests.
= Discovery stage.
= Translation stage.
= Clinically available.
