Chemische Charakterisierung und Spurenanalytik
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Comparing Nontargeted LC-MS Methods by Co-visualizing Linear Dynamic Range and Chemical Coverage
(2019)
INTRODUCTION
Biological and environmental samples contain thousands of small molecule species that all vary in chemical properties and concentration range. Identifying and quantifying all these chemical entities remains a long-term goal in metabolomics and related systems approaches. Due to its broad selectivity, nontargeted LC-MS is usually the method of choice for broad chemical screening. Optimizing nontargeted LC-MS methods, however, is less straightforward than for targeted methods where sensitivity, specificity, linearity etc. serve as well-established performance criteria. We therefore investigated linear dynamic range (LDR) and chemical classification as alternative performance criteria to guide nontargeted method development.
EXPERIMENTAL METHODS
LDR was defined as the linear portion of a feature’s response curve over multiple concentration levels. Comparing the LDR of features across methods can be expected to be significantly more robust than comparing signal intensities for a single concentration. To determine LDR for all features, a computational workflow was implemented in the R programming language. For estimating the linear portion of a curve, several mathematical approaches including linear, non-linear and piecewise linear regression were evaluated. Chemical classification was based on ClassyFire, which computes chemical classes for a given structure. To avoid false classifications for incorrectly annotated compounds, we took the following statistical approach. For each compound, multiple likely annotation hypotheses were derived using a recently described workflow[2]. All annotation hypotheses were submitted to ClassyFire and obtained classifications were ranked by frequency. The most frequently suggested class was kept for further analysis. Finally, LDR and chemical classes were visualized together on a molecular network, which was constructed using the well-established MS/MS similarity approach.
RESULTS AND DISCUSSION
For technical validation of the workflow, several hundred curve fits obtained from the different regression models were reviewed visually. Piecewise linear regression performed the most reliably with respect to the heterogeneous curve shapes of ‘real-life’ features. Validation of chemical classification was performed against a compound library, which showed that 90% of ~450 library compounds were correctly classified using the described approach. Two liquid chromatography methods (HILIC, RPC) as well as two electrospray ionization variants (low/high-temperature ESI) applied to urinary metabolomics were exemplarily studied to test the workflow. Molecular network visualization indicated that of all analytical setups, HILIC/high temperature ESI performed best in terms of high LDR achieved over a wide range of compound classes. Despite one order of magnitude lower sensitivity, HILIC/low temperature ESI showed similar chemical coverage, except for organic nitrogen compounds that were underrepresented compared to high-temperature ESI. Both RPC setups were inferior to the HILIC setups in terms of high-LDR features, supporting previous findings for the given matrix. The higher relative representation of benzenoids and lipids in RPC demonstrated that the workflow successfully captured expected selectivity differences between chromatographies.
CONCLUSION
When comparing nontargeted LC-MS methods for optimization purposes, ideally all available quantitative and qualitative information should be integrated. The present workflow follows this idea. Visualizing LDR and chemical classes of all features on a molecular network quickly indicated differences in method selectivity that were otherwise difficult to spot. As an automated approach, it is easily applied to repeated optimization steps, enabling effective optimization strategies.
Deciphering microbiological influenced corrosion processes on steel with single cell-ICP-ToF-MS
(2022)
Microbiologically influenced corrosion (MIC) is a highly unpredictable process dictated by the environment, microorganisms, and the respective electron source. Interaction pathways between cells and the metal surface remain unclear. The development of this novel single cell-inductively coupled plasma-time of flight-mass spectrometry analytical method and a MIC-specific staining procedure facilitate the investigation of steel-MIC interactions. With this it is possible to analyze the multi-elemental fingerprint of individual cells. The detection method revealed elemental selectivity for the corrosive methanogenic archaeal strain Methanobacterium-affiliated IM1. The interface between material and environmental analysis thus receives special attention, e.g., when considering MIC on solid steel. Hence, the possible uptake of individual elements from different steel samples is investigated. Results showed the cells responded at a single-cell level to the different types of supplemented elements and displayed the abilities to interact with chromium, vanadium, titanium, cobalt, and molybdenum from solid metal surfaces. The information obtained will be used in the future to elucidate underlying mechanisms and develop possible material protection concepts, thus combining modern methods of analytical sciences with materials research.
References.
Inductively coupled plasma-time of flight-mass spectrometry (ICP-ToF-MS) enables the analysis of the multi-element fingerprint of individual cells due to a (quasi-)simultaneous detection of about 70 elements of the periodic table. The interface between material and environmental analysis thus receives special attention, e.g., when considering corrosion processes. Microbiologically influenced corrosion (MIC) is a highly unpredictable phenomenon due to the influence of the environment, microbial communities involved and the respective electron source. However, the interaction pathway between cells and the metal surface remains unclear. The development of the MIC-specific ICP-ToF-MS analytical method presented here, in combination with the investigation of steel-MIC interactions, contributes significantly to progress in instrumental MIC analysis and will enable clarification of the processes taking place. For this, a MIC-specific staining procedure was developed which ensures the analysis of intact cells. It allows the studies of archaea at a single cell level which is extremely scarce compared to other well characterized organisms. Additionally, the single cell ICP-ToF-MS is used for the analysis of archaea involved in MIC of steel. Hence, the possible uptake of individual elements from different steel samples is investigated - the information obtained will be used in the future to elucidate underlying mechanisms and develop possible material protection concepts, thus combining modern methods of analytical sciences with materials research.