Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects

Mass spectrometry focusing on small endogenous molecules has become an integral part of biomarker discovery in the pursuit of an in-depth understanding of the pathophysiology of various diseases, ultimately enabling the application of personalized medicine. While LC-MS methods allow researchers to gather vast amounts of data from hundreds or thousands of samples, the successful execution of a study as part of clinical research also requires knowledge transfer with clinicians, involvement of data scientists, and interactions with various stakeholders. The initial planning phase of a clinical research project involves specifying the scope and design, and engaging relevant experts from different fields. Enrolling subjects and designing trials rely largely on the overall objective of the study and epidemiological considerations, while proper pre-analytical sample handling has immediate implications on the quality of analytical data. Subsequent LC-MS measurements may be conducted in a targeted, semi-targeted, or non-targeted manner, resulting in datasets of varying size and accuracy. Data processing further enhances the quality of data and is a prerequisite for in-silico analysis. Nowadays, the evaluation of such complex datasets relies on a mix of classical statistics and machine learning applications, in combination with other tools, such as pathway analysis and gene set enrichment. Finally, results must be validated before biomarkers can be used as prognostic or diagnostic decision-making tools. Throughout the study, quality control measures should be employed to enhance the reliability of data and increase confidence in the results. The aim of this graphical review is to provide an overview of the steps to be taken when conducting an LC-MS-based clinical research project to search for small molecule biomarkers.

Pre-analytical sample handling standardization for reliable measurement of metabolites and lipids in LC-MS-based clinical research

The emerging disciplines of lipidomics and metabolomics show great potential for the discovery of diagnostic biomarkers, but appropriate pre-analytical sample-handling procedures are critical because several analytes are prone to ex vivo distortions during sample collection. To test how the intermediate storage temperature and storage period of plasma samples from K3EDTA whole-blood collection tubes affect analyte concentrations, we assessed samples from non-fasting healthy volunteers (n = 9) for a broad spectrum of metabolites, including lipids and lipid mediators, using a well-established LC-MS-based platform. We used a fold change-based approach as a relative measure of analyte stability to evaluate 489 analytes, employing a combination of targeted LC-MS/MS and LC-HRMS screening. The concentrations of many analytes were found to be reliable, often justifying less strict sample handling; however, certain analytes were unstable, supporting the need for meticulous processing. We make four data-driven recommendations for sample-handling protocols with varying degrees of stringency, based on the maximum number of analytes and the feasibility of routine clinical implementation. These protocols also enable the simple evaluation of biomarker candidates based on their analyte-specific vulnerability to ex vivo distortions. In summary, pre-analytical sample handling has a major effect on the suitability of certain metabolites as biomarkers, including several lipids and lipid mediators. Our sample-handling recommendations will increase the reliability and quality of samples when such metabolites are necessary for routine clinical diagnosis.

Post-mortem changes of prostanoid concentrations in tissues of mice: Impact of fast cervical dislocation and dissection delay

Prostanoids are potent lipid mediators involved in a wide variety of physiological functions like blood pressure regulation or inflammation as well as cardiovascular and malign diseases. Elucidation of their modes of action is mainly carried out in pre-clinical animal models by quantifying prostanoids in tissues of interest. Unfortunately, prostanoids are prone to post-mortem artifact formation and de novo synthesis can already be caused by external stimuli during the euthanasia of animals like prolonged hypercapnia or ischemia. Therefore, this study investigates the suitability and impact of fast cervical dislocation for the determination of prostanoids (6-keto-PGF_1α, TXB₂, PGF_2α, PGD₂, PGE₂) in seven tissues of mice (spinal cord, brain, sciatic nerve, kidney, liver, lung, and spleen) to minimize time-dependent effects and approximate physiological concentrations. Tissues were dissected in a standardized sequence directly or after 10 min to investigate the influence of dissection delays. The enzyme inhibitor indomethacin (10 µM) in combination with low processing temperatures was employed to preserve prostanoid concentrations during sample preparation. Quantification of prostanoids was performed via LC-MS/MS. This study shows, that prostanoids are differentially susceptible to post-mortem artifact formation which is closely connected to their physiological function and metabolic stability in the respective tissues. Prostanoids in the brain, spinal cord, and kidney that are not involved in the regulatory response post-mortem, i.e. blood flow regulation (6-keto-PGF_1α, PGE₂, PGF_2α) showed high reproducibility even after dissection delay and could be assessed after fast cervical dislocation if prerequisites like standardized pre-analytical workflows with immediate dissection and inhibition of residual enzymatic activity are in place. However, in tissues with high metabolic activity (liver, lung) more stable prostanoid metabolites should be used. Moreover, prostanoids in the spleen were strongly affected by dissection delays and presumably the method of euthanasia itself.

Pre-analytical challenges for the quantification of endocannabinoids in human serum

Endocannabinoids (ECs) are potent lipid mediators with high physiological relevance. They are involved in a wide variety of diseases like depression or multiple sclerosis and are closely connected to metabolic parameters in humans. Therefore, their suitability as a biomarker in different (patho-)physiological conditions is discussed intensively and predominantly investigated by analyzing systemic concentrations in easily accessible matrices like blood. Carefully designed pre-analytical sample handling is of major importance for high-quality data, but harmonization is not achieved yet. Whole blood is either processed to serum or plasma before the onset of analytical workflows and while knowledge about pre-analytical challenges in plasma handling is thorough they were not systematically investigated for serum. Therefore, the ECs AEA and 2-AG, and closely related EC-like substances 1-AG, DHEA, and PEA were examined by LC-MS/MS in serum samples of nine healthy volunteers employing different pre-analytical sample handling protocols, including prolonged coagulation, and storage after centrifugation at room temperature (RT) or on ice. Furthermore, all analytes were also assessed in plasma samples obtained from the same individuals at the same time points to investigate the comparability between those two blood-based matrices regarding obtained concentrations and their 2-AG/1-AG ratio. This study shows that ECs and EC-like substances in serum samples were significantly higher than in plasma and are especially prone to ex vivo changes during initial and prolonged storage for coagulation at RT. Storage on ice after centrifugation is less critical. However, storage at RT further increases 1-AG and 2-AG concentrations, while also lowering the already reduced 2-AG/1-AG ratio due to isomerization. Thus, avoidance of prolonged processing at RT can increase data quality if serum as the matrix of choice is unavoidable. However, serum preparation in itself is expected to initiate changes of physiological concentrations as standard precautionary measures like fast and cooled processing can only be utilized by using plasma, which should be the preferred matrix for analyses of ECs and EC-like substances.