Gas chromatography coupled to high resolution time-of-flight mass spectrometry as a high-throughput tool for characterizing geochemical biomarkers in sediments

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOThe performance of gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC-HRTofMS) for characterizing geochemical biomarkers from sediment samples was evaluated. Two approaches to obtain the geochemical biomarkers were tested:2018FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO2012/21395-0The authors would like to thank FAPESP (scholarship 2012/21395-0) for sponsoring this stud

The Calcitonin and Glucocorticoids Combination: Mechanistic Insights into Their Class-Effect Synergy in Experimental Arthritis

PMCID: PMC3564948This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Dependent Peak Model Based Spectrum Deconvolution for Analysis of High Resolution LC-MS Data

A data dependent peak model (DDPM) based spectrum deconvolution method was developed for analysis of high resolution LC-MS data. To construct the selected ion chromatogram (XIC), a clustering method, the density based spatial clustering of applications with noise (DBSCAN), is applied to all <i>m</i>/<i>z</i> values of an LC-MS data set to group the <i>m</i>/<i>z</i> values into each XIC. The DBSCAN constructs XICs without the need for a user defined <i>m</i>/<i>z</i> variation window. After the XIC construction, the peaks of molecular ions in each XIC are detected using both the first and the second derivative tests, followed by an optimized chromatographic peak model selection method for peak deconvolution. A total of six chromatographic peak models are considered, including Gaussian, log-normal, Poisson, gamma, exponentially modified Gaussian, and hybrid of exponential and Gaussian models. The abundant nonoverlapping peaks are chosen to find the optimal peak models that are both data- and retention-time-dependent. Analysis of 18 spiked-in LC-MS data demonstrates that the proposed DDPM spectrum deconvolution method outperforms the traditional method. On average, the DDPM approach not only detected 58 more chromatographic peaks from each of the testing LC-MS data but also improved the retention time and peak area 3% and 6%, respectively

Gas Chromatography Coupled to High Resolution Time-of-Flight Mass Spectrometry as a High-Throughput Tool for Characterizing Geochemical Biomarkers in Sediments

The performance of gas chromatography coupled to high-resolution time-of-flight mass spectrometry (GC-HRTofMS) for characterizing geochemical biomarkers from sediment samples was evaluated. Two approaches to obtain the geochemical biomarkers were tested: (1) extraction with organic solvent and subsequent derivatization and (2) in-situ derivatization thermal desorption. Results demonstrated that both approaches can be conveniently applied for simultaneous characterization of many geochemical biomarkers (alkanes, alkanols, sterols, and fatty acids), avoiding conventional time-consuming purification procedures. GC-HRTofMS reduces both sample preparation time and the number of chromatographic runs compared to traditional methodologies used in organic geochemistry. Particularly, the approach based on in-situ derivatization thermal desorption represents a very simple method that can be performed in-line employing few milligrams of sediment, eliminating the need for any sample preparation and solvent use. The high resolving power (m/Δm50% 25,000) and high mass accuracy (error ≤ 1 ppm) offered by the “zig-zag” time-of-flight analyzer were indispensable to resolve the complexity of the total ion chromatograms, representing a high-throughput tool. Extracted ion chromatograms using exact m/z were useful to eliminate many isobaric interferences and to increase significantly the signal to noise ratio. Characteristic fragment ions allowed the identification of homologous series, such as alkanes, alkanols, fatty acids, and sterols. Polycyclic aromatic hydrocarbons were also identified in the samples by their molecular ions. The characterization of geochemical biomarkers along a sedimentary core collected in the area of Valo Grande Channel (Cananéia-Iguape Estuarine-Lagunar System (São Paulo, Brazil)) provided evidences of environmental changes. Sediments deposited before opening of channel showed dominance of biomarkers from mangrove vegetation, whereas sediments of the pos-opening period showed an increase of biomarkers from aquatic macrophyte (an invasive vegetation)

Data Preprocessing Method for Liquid Chromatography–Mass Spectrometry Based Metabolomics

A set of data preprocessing algorithms for peak detection and peak list alignment are reported for analysis of liquid chromatography–mass spectrometry (LC–MS)-based metabolomics data. For spectrum deconvolution, peak picking is achieved at the selected ion chromatogram (XIC) level. To estimate and remove the noise in XICs, each XIC is first segmented into several peak groups based on the continuity of scan number, and the noise level is estimated by all the XIC signals, except the regions potentially with presence of metabolite ion peaks. After removing noise, the peaks of molecular ions are detected using both the first and the second derivatives, followed by an efficient exponentially modified Gaussian-based peak deconvolution method for peak fitting. A two-stage alignment algorithm is also developed, where the retention times of all peaks are first transferred into the <i>z</i>-score domain and the peaks are aligned based on the measure of their mixture scores after retention time correction using a partial linear regression. Analysis of a set of spike-in LC–MS data from three groups of samples containing 16 metabolite standards mixed with metabolite extract from mouse livers demonstrates that the developed data preprocessing method performs better than two of the existing popular data analysis packages, MZmine2.6 and XCMS², for peak picking, peak list alignment, and quantification

Integrative mass spectrometry strategy for fingerprinting and tentative structural characterization of asphaltenes

FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPEAM - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO AMAZONASCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORHeavy oils are characterized by high proportions of asphaltenes, a class of polyaromatic hydrocarbons whose structures remain unknown. Previous work based on asphaltene physical-chemical properties and extensive spectroscopic and computational methods has220717724FAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPEAM - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO AMAZONASCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORFAPESP - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULOFAPEAM - FUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DO AMAZONASCAPES - COORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIORsem informaçãosem informaçãosem informaçãoWe acknowledge FAPESP, FAPEAM, and PETROBRAS for their financial support. HHFK acknowledges CAPES for fellowship suppor