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Small molecule biomarker discovery: Proposed workflow for LC-MS-based clinical research projects
(2023)
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.
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.
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.
Identifying co-expression of lipid species is challenging, but indispensable to identify novel therapeutic targets for breast cancer treatment. Lipid metabolism is often dysregulated in cancer cells, and changes in lipid metabolism affect cellular processes such as proliferation, autophagy, and tumor development. In addition to mRNA analysis of sphingolipid metabolizing enzymes, we performed liquid chromatography time-of-flight mass spectrometry analysis in three breast cancer cell lines. These breast cancer cell lines differ in estrogen receptor and G-protein coupled estrogen receptor 1 status. Our data show that sphingolipids and non-sphingolipids are strongly increased in SKBr3 cells. SKBr3 cells are estrogen receptor negative and G-protein coupled estrogen receptor 1 positive. Treatment with G15, a G-protein coupled estrogen receptor 1 antagonist, abolishes the effect of increased sphingolipid and non-sphingolipid levels in SKBr3 cells. In particular, ether lipids are expressed at much higher levels in cancer compared to normal cells and are strongly increased in SKBr3 cells. Our analysis reveals that this is accompanied by increased sphingolipid levels such as ceramide, sphingadiene-ceramide and sphingomyelin. This shows the importance of focusing on more than one lipid class when investigating molecular mechanisms in breast cancer cells. Our analysis allows unbiased screening for different lipid classes leading to identification of co-expression patterns of lipids in the context of breast cancer. Co-expression of different lipid classes could influence tumorigenic potential of breast cancer cells. Identification of co-regulated lipid species is important to achieve improved breast cancer treatment outcome.
Introduction: Arachidonoyl ethanolamide (AEA) and 2-arachidonoyl glycerol (2-AG) are central lipid mediators of the endocannabinoid system. They are highly relevant due to their involvement in a wide variety of inflammatory, metabolic or malign diseases. Further elucidation of their modes of action and use as biomarkers in an easily accessible matrix, like blood, is restricted by their susceptibility to deviations during blood sampling and physiological co-dependences, which results in high variability of reported concentrations in low ng/mL ranges.
Objectives: The objective of this review is the identification of critical parameters during the pre-analytical phase and proposal of minimum requirements for reliable determination of endocannabinoids (ECs) in blood samples.
Methods: Reported physiological processes influencing the EC concentrations were put into context with published pre-analytical research and stability data from bioanalytical method validation.
Results: The cause for variability in EC concentrations is versatile. In part, they are caused by inter-individual factors like sex, metabolic status and/or diurnal changes. Nevertheless, enzymatic activity in freshly drawn blood samples is the main reason for changing concentrations of AEA and 2-AG, besides additional non-enzymatic isomerization of the latter.
Conclusion: Blood samples for EC analyses require immediate processing at low temperatures (>0 °C) to maintain sample integrity. Standardization of the respective blood tube or anti-coagulant, sampling time point, applied centrifugal force and complete processing time can further decrease variability caused by sample handling. Nevertheless, extensive characterization of study participants is needed to reduce distortion of clinical data caused by co-variables and facilitate research on the endocannabinoid system.