Valerie is an Honorary Group Leader, currently based at the the University of Cardiff. She is working with Group Leaders in the Signalling Programme. Valerie’s research is focused on the study of bioactive lipids in circulating blood cells, particularly platelets. She uses mass spectrometry to identify and characterise new signalling mediators. Since 2007, her group identified large families of lipids made by platelets, neutrophils and monocytes, and demonstrated that these are involved in innate immunity, blood clotting, cardiovascular disease and infection. Translational studies have established a role for these lipids in human thrombotic disease.
She has developed new methods to quantify aminophospholipids at the platelet surface, for example molecular species of amino-phospholipids and oxidized phospholipids that comprise the pro-coagulant surface, which is essential for blot formation. She leads/led a programme grant from British Heart Foundation (renewed 2020), and was an ERC Advanced Grant holder (2014-2019). Since 2017, she has been co-lead of LIPID MAPS, a multi-site Biomedical Resource supporting databases, tools, nomenclature and curation of lipids, funded by Wellcome Trust (>66K users, with Edward Dennis and Shankar Subramaniam (UCSD), Simon Andrews and Andrea Lopez, Babraham, and Bill Griffiths, Swansea).
She was Co-Director of the Systems Immunity Research Institute, Cardiff University from 2016-2020. She is co-investigator on an EU Marie Curie ITN (ArthritisHeal, Leiden), an EU Cost Network (EpiLipidNET) and an MRC Partnership Grant (MAP/UK, Imperial), and an Associate Group Lead at UK Dementia Research Institute (UKDRI) at Cardiff University.
In this community effort, we compare measurements between 34 laboratories from 19 countries, utilizing mixtures of labelled authentic synthetic standards, to quantify by mass spectrometry four clinically used ceramide species in the NIST (National Institute of Standards and Technology) human blood plasma Standard Reference Material (SRM) 1950, as well as a set of candidate plasma reference materials (RM 8231). Participants either utilized a provided validated method and/or their method of choice. Mean concentration values, and intra- and inter-laboratory coefficients of variation (CV) were calculated using single-point and multi-point calibrations, respectively. These results are the most precise (intra-laboratory CVs ≤ 4.2%) and concordant (inter-laboratory CVs < 14%) community-derived absolute concentration values reported to date for four clinically used ceramides in the commonly analyzed SRM 1950. We demonstrate that calibration using authentic labelled standards dramatically reduces data variability. Furthermore, we show how the use of shared RM can correct systematic quantitative biases and help in harmonizing lipidomics. Collectively, the results from the present study provide a significant knowledge base for translation of lipidomic technologies to future clinical applications that might require the determination of reference intervals (RIs) in various human populations or might need to estimate reference change values (RCV), when analytical variability is a key factor for recall during multiple testing of individuals.
LIPID MAPS (LIPID Metabolites and Pathways Strategy), www.lipidmaps.org, provides a systematic and standardized approach to organizing lipid structural and biochemical data. Founded 20 years ago, the LIPID MAPS nomenclature and classification has become the accepted community standard. LIPID MAPS provides databases for cataloging and identifying lipids at varying levels of characterization in addition to numerous software tools and educational resources, and became an ELIXIR-UK data resource in 2020. This paper describes the expansion of existing databases in LIPID MAPS, including richer metadata with literature provenance, taxonomic data and improved interoperability to facilitate FAIR compliance. A joint project funded by ELIXIR-UK, in collaboration with WikiPathways, curates and hosts pathway data, and annotates lipids in the context of their biochemical pathways. Updated features of the search infrastructure are described along with implementation of programmatic access via API and SPARQL. New lipid-specific databases have been developed and provision of lipidomics tools to the community has been updated. Training and engagement have been expanded with webinars, podcasts and an online training school.
Targeted metabolite assays that measure tens or hundreds of pre-selected metabolites, typically using liquid chromatography-mass spectrometry, are increasingly being developed and applied to metabolic phenotyping studies. These are used both as standalone phenotyping methods and for the validation of putative metabolic biomarkers obtained from untargeted metabolomics studies. However, there are no widely accepted standards in the scientific community for ensuring reliability of the development and validation of targeted metabolite assays (referred to here as 'targeted metabolomics'). Most current practices attempt to adopt, with modifications, the strict guidance provided by drug regulatory authorities for analytical methods designed largely for measuring drugs and other xenobiotic analytes. Here, the regulatory guidance provided by the European Medicines Agency, US Food and Drug Administration and International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use are summarized. In this Perspective, we have adapted these guidelines and propose a less onerous 'tiered' approach to evaluate the reliability of a wide range of metabolomics analyses, addressing the need for community-accepted, harmonized guidelines for tiers other than full validation. This 'fit-for-purpose' tiered approach comprises four levels-discovery, screening, qualification and validation-and is discussed in the context of a range of targeted and untargeted metabolomics assays. Issues arising with targeted multiplexed metabolomics assays, and how these might be addressed, are considered. Furthermore, guidance is provided to assist the community with selecting the appropriate degree of reliability for a series of well-defined applications of metabolomics.