Targeted quantitative metabolic profiling of brain-derived cell cultures by semi-automated MEPS and LC-MS/MS

The accurate characterisation of metabolic profiles is an important prerequisite to determine the rate and the efficiency of the metabolic pathways taking place in the cells. Changes in the balance of metabolites involved in vital processes such as glycolysis, tricarboxylic acid (TCA) cycle, oxidative phosphorylation (OXPHOS), as well as in the biochemical pathways related to amino acids, lipids, nucleotides, and their precursors reflect the physiological condition of the cells and may contribute to the development of various human diseases. The feasible and reliable measurement of a wide array of metabolites and biomarkers possesses great potential to elucidate physiological and pathological mechanisms, aid preclinical drug development and highlight potential therapeutic targets. An effective, straightforward, sensitive, and selective liquid chromatography-tandem mass spectrometry (LC-MS/MS) approach was developed for the simultaneous quali-quantitative analysis of 41 compounds in both cell pellet and cell growth medium obtained from brain-derived cell cultures. Sample pretreatment miniaturisation was achieved thanks to the development and optimisation of an original extraction/purification approach based on digitally programmed microextraction by packed sorbent (eVol®-MEPS). MEPS allows satisfactory and reproducible clean-up and preconcentration of both low-volume homogenate cell pellet lysate and cell growth medium with advantages including, but not limited to, minimal sample handling and method sustainability in terms of sample, solvents, and energy consumption. The MEPS-LC-MS/MS method showed good sensitivity, selectivity, linearity, and precision. As a proof of concept, the developed method was successfully applied to the analysis of both cell pellet and cell growth medium obtained from a line of mouse immortalised oligodendrocyte precursor cells (OPCs; Oli-neu cell line), leading to the unambiguous determination of all the considered target analytes. This method is thus expected to be suitable for targeted, quantitative metabolic profiling in most brain cell models, thus allowing accurate investigations on the biochemical pathways that can be altered in central nervous system (CNS) neuropathologies, including e.g., mitochondrial respiration and glycolysis, or use of specific nutrients for growth and proliferation, or lipid, amino acid and nucleotide metabolism

Innovative Solid-Phase Extraction Strategies for Improving the Advanced Chromatographic Determination of Drugs in Challenging Biological Samples

In the past few decades, considerable scientific strides have been made in the subject of drug analysis in human biological samples. However, the risk caused by incorrect drug plasma levels in patients still remains an important concern. This review paper attempts to investigate the advances made over the last ten years in common sample preparation techniques (SPT) for biological samples based on solid sorbents, including solid-phase extraction (SPE) and solid-phase micro-extraction (SPME), and in particular in the field of molecularly imprinted polymers (MIPs), including non-stimuli-responsive and stimuli-responsive adsorbents. This class of materials is known as 'smart adsorbents', exhibiting tailored responses to various stimuli such as magnetic fields, pH, temperature, and light. Details are provided on how these advanced SPT are changing the landscape of modern drug analysis in their coupling with liquid chromatography-mass spectrometry (LC-MS) analytical techniques, a general term that includes high-performance liquid chromatography (HPLC) and ultra-high performance liquid chromatography (UHPLC), as well as any variation of MS, such as tandem (MS/MS), multiple-stage (MSn), and high-resolution (HRMS) mass spectrometry. Some notes are also provided on coupling with less-performing techniques, such as high-performance liquid chromatography with ultraviolet (HPLC-UV) and diode array detection (HPLC-DAD) detection. Finally, we provide a general review of the difficulties and benefits of the proposed approaches and the future prospects of this research area