A data-driven approach for the detection of internal standard outliers in targeted LC-MS/MS assays

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.

Improved LC/MS/MS Quantification Using Dual Deuterated Isomers as the Surrogates: A Case Analysis of Enrofloxacin Residue in Aquatic Products

Extensive and high residue variations in enrofloxacin (ENR) exist in different aquatic products. A novel quantitative method for measuring ENR using high-performance liquid chromatography-tandem mass spectrometry was developed employing enrofloxacin-d₅ (ENR-d₅) and enrofloxacin-d₃ (ENR-d₃) as isotope surrogates. This reduced the deviation of detected values, which results from the overpass of the linear range and/or the large difference in the residue between the isotope standard and ENR, from the actual content. Furthermore, high residue levels of ENR can be directly diluted and re-calibrated by the corresponding curve with the addition of high levels of another internal surrogate without repeated sample preparation, avoiding the overflow of the instrument response. The validation results demonstrated that the method can simultaneously determine ENR residues from MQL (2 µg/kg) to 5000 × MQL (method quantification limit) with recoveries between 97.1 and 106%, and intra-precision of no more than 2.14%. This method realized a wide linear calibration range with dual deuterated isomers, which has not been previously reported in the literature. The developed method was successfully applied to the analysis of ENR in different aquatic products, with ENR residue levels varying from 108 to 4340 μg/kg and an interval of precision in the range of 0.175~6.72%. These results demonstrate that batch samples with a high variation in ENR residues (over the linear range with a single isotope standard) can be detected by the dual isotope surrogates method in a single sample preparation process.