= Emerging. More than 5 years before clinical availability. (24.37%, 2023)
= Expected to be clinically available in 1 to 4 years. (39.50%, 2023)
= Clinically available now. (36.13%, 2023)
MSACL 2023 : Hahnefeld

MSACL 2023 Abstract

Self-Classified Topic Area(s): Pre-Analytics > Lipidomics > Metabolomics

Podium Presentation in Steinbeck 2 on Wednesday at 14:40 (Chair: Anne Bendt / Frederick Strathmann)

Lipidomics and Biobanking: Challenges of Pre-Analytical Sample Handling

Lisa Hahnefeld (1,2), Samuel Rischke (2), Alena Sens (2), Stephan M. G. Schäfer (1,2), Dominique Thomas (1,2), Michaela Köhm (1,3), Frank Behrens (1,3), Gerd Geisslinger (1,2), Robert Gurke (1,2)
(1) Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, and Fraunhofer Cluster of Excellence for Immune Mediated Diseases CIMD, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany (2) pharmazentrum frankfurt/ZAFES, Institute of Clinical Pharmacology, Johann Wolfgang Goethe University, Theodor Stern-Kai 7, 60590 Frankfurt am Main, Germany (3) Rheumatology, University Hospital Frankfurt, Theodor-Stern-Kai 7, 60596 Frankfurt am Main, Germany

Lisa Hahnefeld (Presenter)
Fraunhofer Institute for Translational Medicine and Pharmacology ITMP, Frankfurt, Germany; Pharmazentrum Frankfurt/ Zafes, Institute of Clinical Pharmacology, Goethe University, Frankfurt, Germany

Presenter Bio: Lisa Hahnefeld received her state exam in pharmacy at the Goethe University Frankfurt, Germany in 2014. After a short period as pharmaceutical intern in the pharmaceutical industry and a pharmacy, she finished her doctoral thesis in 2020 at the Institute of Clinical Pharmacology in Frankfurt under the guidance of Prof. Geisslinger, where she continued her work as a research associate. Ms Hahnefeld is performing LC-HRMS analysis since starting her doctoral thesis and gained broad experience in the field of lipidomics, metabolomics and small exogenous molecules.


Lipid analysis in clinical research can improve our understanding of the pathophysiological processes responsible for a respective disease, as well as identify potential biomarkers, e.g. to make early diagnosis or diagnosis at all possible. Yet, the necessary clinical trials are complex, expensive, and time-consuming. Therefore, usage of already existing plasma samples from biobanks is a common way to save both time and money. However, the applied pre-analytical protocols to obtain these samples seldom consider lipid stability. The strict pre-analytical requirements for lipid analysis result in limited usability of such samples.

The aim of our study was to set pre-analytical protocols covering different clinical settings and lipid classes, while ensuring the stability of these lipids. Furthermore, a database containing data on pre-analytical lipid stability should be established to support retrospective evaluation of sample quality.
Methods: Human K3EDTA whole blood samples were stored before and after centrifugation for 0 min, 20 min, 60 min, 120 min and 240 min, either at room temperature or in ice water. Resulting plasma samples were analyzed using a well-established LC-MS platform for measuring metabolites, lipids and lipid mediators like endocannabinoids, oxylipins, sphingolipids and lysophosphatidic acids. Using the data from this study in combination with previously published results, usability of samples from a biobank for lipidomic analysis can be evaluated by comparison of fold changes to directly processed samples using a R shiny app.

The results of our study highlight once again the high impact of pre-analytical sample processing conditions on ex vivo concentration changes of lipids from various lipid classes as well as other polar metabolites. However, not all lipids are equally vulnerable to pre-analytical concentration changes in whole blood or plasma as for example ceramides, sphingomyelins and triglycerides are highly stable compared to other lipids like endocannabinoids or lysophosphatidic acids. Therefore, we defined four different pre-analytical protocols based on vulnerability of metabolites and lipids to ex vivo concentration changes. Out of 489 metabolites, 456 metabolites can be measured with high quality in samples, which were processed within 60 min before and after centrifugation under cooled conditions. While extending the processing time to 240 min each, still 452 analytes can be analyzed. In settings, where cooling is not possible, measurable metabolites are reduced to 428 with 60 min or 377 with 240 min of processing times before and after centrifugation, respectively. These data were combined with eight previously published studies, resulting in a database containing more than 23.000 fold changes for > 1000 metabolites and 110 pre-analytical conditions for EDTA and serum samples so far. The database can be accessed via a R shiny app.

Beside suggesting analyte specific prospective sampling protocols, our R shiny app allows users to evaluate the usability of samples from biobanks for lipidomic analysis, simplifying data-driven decisions concerning pre-analytics.

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