Automated combustion accelerator mass spectrometry for the analysis of biomedical samples in the low attomole range.

The increasing role of accelerator mass spectrometry (AMS) in biomedical research necessitates modernization of the traditional sample handling process. AMS was originally developed and used for carbon dating, therefore focusing on a very high precision but with a comparably low sample throughput. Here, we describe the combination of automated sample combustion with an elemental analyzer (EA) online coupled to an AMS via a dedicated interface.

Identifying inhibitory compounds in lignocellulosic biomass hydrolysates using an exometabolomics approach.

Background: Inhibitors are formed that reduce the fermentation performance of fermenting yeast during the pretreatment process of lignocellulosic biomass. An exometabolomics approach was applied to systematically identify inhibitors in lignocellulosic biomass hydrolysates.

Results: We studied the composition and fermentability of 24 different biomass hydrolysates.

Pre-processing liquid chromatography/high-resolution mass spectrometry data: extracting pure mass spectra by deconvolution from the invariance of isotopic distribution.

Rationale: Mass spectra obtained by deconvolution of liquid chromatography/high-resolution mass spectrometry (LC/HRMS) data can be impaired by non-informative mass-over-charge (m/z) channels. This impairment of mass spectra can have significant negative influence on further post-processing, like quantification and identification.

Methods: A metric derived from the knowledge of errors in isotopic distribution patterns, and quality of the signal within a pre-defined mass chromatogram block, has been developed to pre-select all informative m/z channels.

Analysis of oligosaccharides in lignocellulosic biomass hydrolysates by high-performance anion-exchange chromatography coupled with mass spectrometry (HPAEC-MS).

The carbohydrate composition of lignocellulosic biomass hydrolysates is highly complex. High performance anion exchange chromatography coupled with pulsed amperometric detection (HPAEC-PAD), a widely used method for carbohydrate analysis, provides limited chemical information on the detected peaks. To improve the detection and increase the chemical information of the carbohydrates, HPAEC was coupled with mass spectrometry (MS).

Automated pipeline for de novo metabolite identification using mass-spectrometry-based metabolomics.

Metabolite identification is one of the biggest bottlenecks in metabolomics. Identifying human metabolites poses experimental, analytical, and computational challenges. Here we present a pipeline of previously developed cheminformatic tools and demonstrate how it facilitates metabolite identification using solely LC/MS(n) data.