No abstract
Given the intricate organization of the brain, tissue sampling for chemical profiling studies have always been a challenging task. It is often exceptionally difficult to obtain homogeneous samples for in vitro/ex vivo experiments without altering or losing valuable information. The obvious approach has been to develop in vivo analytical methods that may cause minimal perturbation to this complex chemical network so as to improve overall reliability of acquired information. Methods such as biosensors and microdialysis (MD) are among sampling methods applied to in vivo brain chemical profiling studies despite their unique challenges. MD is a well-established in vivo analytical sampling method used over the years for monitoring often low-molecular-weight hydrophilic compounds from the interstitial space. The successful application of the method to neuroscience, especially monitoring of neurotransmitters, led to its expansion to a wider range of analytes, including drugs, [1] metabolites, [2] and peptides. [3] A major challenge, however, associated with MD is its difficulty in sampling hydrophobic compounds. Hydrophobic compounds are often highly protein-bound and bind to the MD probe and tubing, thereby affecting relative recovery. The addition of modifiers, such as bovine serum albumin, glycerol in water, [4] or cyclodextrin, [5] is among the approaches that have been used to prevent hydrophobic interactions and to improve relative recoveries. But these techniques often may complicate the pharmacology of the neurological analytes, as the additives are known to interact with the tissue surrounding the probe. [6] Thus, in typical global metabolomics studies, for example, the composition of a measured metabolome can be significantly affected by the analytical procedure, leaving the analysts with results which likely do not adequately reflect accurate composition of the metabolome during sampling. [7] In effect, it will compromise the already challenging efforts in diagnosis, prognostics, and searching for potential biomarkers for therapeutic purposes. Herein, we demonstrate a novel application of solid-phase microextraction (SPME) for in vivo sampling for brain study. For the first time an application of in vivo SPME as a complementary method to MD for braintissue bioanalysis has been presented. Our technique was first validated against MD in targeted analysis of selected neurotransmitters. Their complementary nature was subsequently shown in global profiling of the brain metabolome. From the profiling study, SPME detected groups of lipids such as gangliosides, fatty acids, and lysophospholipids, which are of particular interest in relation to neurodegenerative diseases. SPME derives its selectivity from the extracting sorbent type. Thus, SPME provides the needed flexibility to analysts to tailor investigations to specific biologically hydrophilic/ hydrophobic compounds. For a global study of the metabolome, however, the sorbent choice is one of low selectivity; that is, the sorbent chemical property must enhance simu...
The automation of solid-phase microextraction (SPME) coupled to liquid chromatography-tandem mass spectrometry (LC-MS/MS) was accomplished using a 96 multiwell plate format, a SPME multifiber device, two orbital shakers, and a three-arm robotic system. Extensive optimization of the proposed setup was performed including coating selection, optimization of the fiber coating procedure, confirmation of uniform agitation in all wells, and the selection of the optimal calibration method. The system allows the use of pre-equilibrium extraction times with no deterioration in method precision due to reproducible timing of extraction and desorption steps and reproducible positioning of all fibers within the wells. The applicability of the system for the extraction of several common drugs is demonstrated. The optimized multifiber SPME-LC-MS/MS was subsequently fully validated for the high-throughput analysis of diazepam, lorazepam, nordiazepam, and oxazepam in human whole blood. The proposed method allowed the automated sample preparation of 96 samples in 100 min, which represents the highest throughput of any SPME technique to date, while achieving excellent accuracy (87-113%), precision (
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