“Targeted Metabolomics” in der Biomarkerforschung

Griffiths, William J.; Koal, Therese; Kohl, Matthias; Enot, David; Deigner, Hans‐Peter

doi:10.1002/ange.200905579

Cited by 8 publications

(3 citation statements)

References 178 publications

(148 reference statements)

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“…Liquid chromatography coupled with mass spectrometry (LC–MS) is the major platform for analyzing small molecules. , The liquid chromatography separates a complex sample into individual compounds, and the mass spectrometry (MS) identifies them by measuring mass-to-charge ratio ( m / z ) of their ions. With the high separation ability of liquid chromatography and accurate m / z measurement of modern MS, LC–MS is applied in small molecule identification extensively. − …”

Section: Introductionmentioning

confidence: 99%

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

Yang

Lü

et al. 2021

Anal. Chem.

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The predicted liquid chromatographic retention times (RTs) of small molecules are not accurate enough for wide adoption in structural identification. In this study, we used the graph neural network to predict the retention time (GNN-RT) from structures of small molecules directly without the requirement of molecular descriptors. The predicted accuracy of GNN-RT was compared with random forests (RFs), Bayesian ridge regression, convolutional neural network (CNN), and a deep-learning regression model (DLM) on a METLIN small molecule retention time (SMRT) dataset. GNN-RT achieved the highest predicting accuracy with a mean relative error of 4.9% and a median relative error of 3.2%. Furthermore, the SMRT-trained GNN-RT model can be transferred to the same type of chromatographic systems easily. The predicted RT is valuable for structural identification in complementary to tandem mass spectra and can be used to assist in the identification of compounds. The results indicate that GNN-RT is a promising method to predict the RT for liquid chromatography and improve the accuracy of structural identification for small molecules.

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Section: Introductionmentioning

confidence: 99%

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

Yang

Lü

et al. 2021

Anal. Chem.

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“…Analysis of metabolites in any sample type has been termed metabolomics, which is the newest major “omics” research field in the postgenomic era . Owing to its unparalleled dynamic range and sensitivity, mass spectrometry is the principal technique employed for simultaneous investigation of the complex mixture of metabolites in human samples .…”

Section: Figurementioning

confidence: 99%

Chemoselective Probe Containing a Unique Bioorthogonal Cleavage Site for Investigation of Gut Microbiota Metabolism

et al. 2018

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While metabolites derived from gut microbiota metabolism have been linked to disease development in the human host, the chemical tools required for their detailed analysis and the discovery of biomarkers are limited. A unique and multifunctional chemical probe for mass spectrometric analysis, which contains p‐nitrocinnamyloxycarbonyl as a new bioorthogonal cleavage site has been designed and synthesized. Coupled to magnetic beads, this chemical probe allows for straightforward extraction of metabolites from human samples and release under mild conditions. This isolation from the sample matrix results in significantly reduced ion suppression, an increased mass spectrometric sensitivity, and facilitates the detection of metabolites in femtomole quantities. The chemoselective probe was applied to the analysis of human fecal samples, resulting in the discovery of four metabolites previously unreported in this sample type and confirmation of the presence of medically relevant gut microbiota‐derived metabolites.

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“…As a result, the number of journal articles featuring SPME as an analytical method for various applications (both qualitative and quantitative) has exceeded 5000 over the past decade, indicative of a mature and well‐understood technique. The main focus of this Minireview is to discuss the emerging opportunities for the use of SPME in the field of metabolomics,2 and to show the capability of this technique to provide valuable information not easily obtained by conventional methodologies.…”

Section: Introduction To Solid‐phase Microextractionmentioning

confidence: 99%

In Vivo Solid‐Phase Microextraction in Metabolomics: Opportunities for the Direct Investigation of Biological Systems

2011

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Sample preparation has a strong impact on the quality of metabolomics studies. The use of solid-phase microextraction (SPME), particularly its in vivo format, enables the capture of a more representative metabolome and presents opportunities to detect low-abundance, short-lived, and/or unstable species not easily captured by traditional methods. The technique is ideally suited for temporal, spatial, and longitudinal studies of the same living system, as well as multicompartmental studies of the same organism. SPME is useful for the investigation of biological systems ranging in complexity from cells to mammalian tissues. Selected examples are highlighted in this Minireview in order to place the technique within the context of conventional methods of sample preparation for metabolomics.

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“Targeted Metabolomics” in der Biomarkerforschung

Cited by 8 publications

References 178 publications

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

Prediction of Liquid Chromatographic Retention Time with Graph Neural Networks to Assist in Small Molecule Identification

Chemoselective Probe Containing a Unique Bioorthogonal Cleavage Site for Investigation of Gut Microbiota Metabolism

In Vivo Solid‐Phase Microextraction in Metabolomics: Opportunities for the Direct Investigation of Biological Systems

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