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Breathomics and its Application for Disease Diagnosis: A Review of Analytical Techniques and Approaches
Published in Raquel Cumeras, Xavier Correig, Volatile organic compound analysis in biomedical diagnosis applications, 2018
David J. Beale, Oliver A. H. Jones, Avinash V. Karpe, Ding Y. Oh, Iain R. White, Konstantinos A. Kouremenos, Enzo A. Palombo
Spectroscopy is based on the measurement of absorption of electromagnetic radiation by a compound, or compounds of interest. Spectral fingerprints of compounds in breath span the UV to the mid-IR spectral regions. Typically, exhaled breath samples require pre-concentration prior to analysis via SPME, a suitable absorbent material or by direct cryofocusing (Miekisch and Schubert, 2006). Compounds present in exhaled breath that are IR or UV active such as ammonia, carbon monoxide, carbon dioxide, methane, and ethane absorb light at wavelengths characteristic of the bonds present in the molecule and these absorption bands can be used to identify specific molecular components and/or to allow identification of a compound via reference library matching. Stable isotopomers of IR active molecules can also be accurately detected, making it possible to follow specific metabolic processes. While infrared spectroscopy data are not as detailed as those from NMR or MS-based methods, the technique has the advantages that it is quick, simple, non-destructive and does not require extensive sample preparation; near real-time data can be obtained and the instruments are much lower in cost that NMR or MS instruments. The disadvantages are that spectroscopy is not as sensitive or selective as MS, with detection limits in the ppm to ppb range and the technique is also limited in the number of chemical species it can distinguish.
Assessment of Myocardial Metabolism with Magnetic Resonance Spectroscopy
Published in Robert J. Gropler, David K. Glover, Albert J. Sinusas, Heinrich Taegtmeyer, Cardiovascular Molecular Imaging, 2007
One of the first applications of 13C-MRS was in the assessment of myocardial oxidative metabolism (39,40). Isotopomer analysis of the 13C-13C splitting patterns (due to J-coupling of adjacent nuclei) of glutamate spectra were used to estimate substrate contribution to the citric acid cycle. By varying the 13C-label distribution in the substrates for energy metabolism, it is possible to distinguish their relative contributions to citric acid cycle turnover. However, direct examination of the citric acid cycle intermediates is not possible with MRS due to the relatively low concentrations for most of these metabolites. However, glutamate is in rapid exchange with α-ketoglutarate, and reflects the label in this intermediate. Different labeling patterns in acetyl-CoA give rise to different labeling patterns in glutamate, permitting the calculation of relative contributions of various substrates.
Insights and prospects for ion mobility-mass spectrometry in clinical chemistry
Published in Expert Review of Proteomics, 2022
David C. Koomen, Jody C. May, John A. McLean
SLIM is an ion optical circuit board architecture supporting extended path lengths of ion trajectories to facilitate higher resolution in ion mobility analyses [130]. SLIM is well-suited for interfacing with both MS and LC-MS, as well as providing a method for determining IM-derived CCS values [51,52,131]. While conventional mobility technologies such as TWIMS and DTIMS have a maximum resolving power typically between 40 and 60, SLIM has the capability of operating at resolving powers of ~200 or greater [132–134]. This allows improved resolution of clinically relevant isomers of different chemical classes at a much higher efficiency than previously described IM techniques. In addition, with SLIM geometries incorporating a cyclic return path for ions, multi-pass SLIM experiments are possible, which can further enhance the resolution of select analytes of interest after each successive pass [132,133]. Presently, the highest recorded IM resolving power in SLIM was ~340 with a separation power of 1860, demonstrated using 40 passes across a distance of about half a kilometer (~540 m) [133]. This has allowed isotopomers (i.e. amino acids incorporating heavy vs. light isotopes) to be resolved with up to a ~0.4% difference in mobility [135]. Heavy-labeling experiments are currently utilized in many clinical mass spectrometry applications and direct differentiation of isotopically labeled analytes by HRIM will directly benefit clinical applications. Following extensive development at Pacific Northwest National Laboratories, SLIM is now commercially available from MOBILion Systems.
Dysbiotic human oral microbiota alters systemic metabolism via modulation of gut microbiota in germ-free mice
Published in Journal of Oral Microbiology, 2022
Kyoko Yamazaki, Eiji Miyauchi, Tamotsu Kato, Keisuke Sato, Wataru Suda, Takahiro Tsuzuno, Miki Yamada-Hara, Nobuo Sasaki, Hiroshi Ohno, Kazuhisa Yamazaki
Metabolome analysis was conducted according to HMT’s Basic Scan package, using capillary electrophoresis time-of-flight mass spectrometry (CE-TOFMS) based on previously described methods [16,17]. CE-TOFMS analysis was conducted using an Agilent CE capillary electrophoresis system equipped with an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Inc., Santa Clara, CA). The systems were controlled by the Agilent G2201AA ChemStation software version B.03.01 (Agilent Technologies) and connected by a fused silica capillary (50μm i.d.×80cm total length) with a commercial electrophoresis buffer (H3301-1001 and I3302-1023 for cation and anion analyses, respectively, HMT) as the electrolyte. The spectrometer was scanned from m/z 50 to 1,000, and the peaks were extracted using the MasterHands automatic integration software (Keio University, Tsuruoka, Yamagata, Japan) to obtain peak information, including m/z, peak area, and migration time (MT) [18]. Signal peaks corresponding to isotopomers, adduct ions, and other product ions of known metabolites were excluded, and the remaining peaks were annotated according to the HMT metabolite database, based on their m/z values and MTs. Subsequently, the areas of the annotated peaks were normalized to the internal standards and sample amounts to obtain the relative levels of each metabolite. We absolutely quantified 277 primary metabolites based on one-point calibrations using their respective standard compounds. Hierarchical cluster analysis and principal component analysis (PCA) [19] were performed using proprietary MATLAB and R programs, respectively. Detected metabolites were plotted on metabolic pathway maps using the VANTED software [20].
Limosilactobacillus reuteri DS0384 promotes intestinal epithelial maturation via the postbiotic effect in human intestinal organoids and infant mice
Published in Gut Microbes, 2022
Hana Lee, Kwang Bo Jung, Ohman Kwon, Ye Seul Son, Eunho Choi, Won Dong Yu, Naeun Son, Jun Hyoung Jeon, Hana Jo, Haneol Yang, Yeong Rak Son, Chan-Seok Yun, Hyun-Soo Cho, Sang Kyu Kim, Dae-Soo Kim, Doo-Sang Park, Mi-Young Son
To extract ionic metabolites, 80 µL of cell-free supernatants were mixed with 20 μL of Milli-Q water containing internal standards (1 mM). Metabolome analysis of three L. reuteri strains (DS0384, KCTC3594T, and DS0195) was conducted using the HMT Basic Scan package using CE-TOFMS according to previously described methods.62,63 Briefly, CE-TOFMS analysis was performed using an Agilent CE capillary electrophoresis system equipped with an Agilent 6210 time-of-flight mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). The systems were controlled by Agilent G2201AA ChemStation software version B.03.01 (Agilent Technologies) and connected by a fused silica capillary (50μm i.d.×80cm total length) with commercial electrophoresis buffer (H3301-1001 and I3302-1023 for cation and anion analyses, respectively, HMT) as the electrolyte. The spectrometer was scanned from m/z 50 to 1,000, and peaks were extracted using MasterHands, automatic integration software (Keio University, Tsuruoka, Yamagata, Japan) to obtain peak information including the m/z, peak area, and migration time.64 Signal peaks corresponding to isotopomers, adduct ions, and other product ions of known metabolites were excluded, and remaining peaks were annotated according to the HMT metabolite database based on their m/z values and MTs. The areas of the annotated peaks were normalized to internal standards and sample amounts to obtain the relative levels of each metabolite. The primary 110 metabolites were quantified based on one-point calibrations using their respective standard compounds. Hierarchical cluster analysis and principal component analysis were performed using proprietary MATLAB and R programs, respectively.65 Detected metabolites were plotted on metabolic pathway maps using VANTED software.66