Visualizing metabolic network dynamics through time-series metabolomic data.

BACKGROUND: New technologies have given rise to an abundance of -omics data, particularly metabolomic data. The scale of these data introduces new challenges for the interpretation and extraction of knowledge, requiring the development of innovative computational visualization methodologies. Here, we present GEM-Vis, an original method for the visualization of time-course metabolomic data within the context of metabolic network maps. We demonstrate the utility of the GEM-Vis method by examining previously published data for two cellular systems-the human platelet and erythrocyte under cold storage for use in transfusion medicine. RESULTS: The results comprise two animated videos that allow for new insights into the metabolic state of both cell types. In the case study of the platelet metabolome during storage, the new visualization technique elucidates a nicotinamide accumulation that mirrors that of hypoxanthine and might, therefore, reflect similar pathway usage. This visual analysis provides a possible explanation for why the salvage reactions in purine metabolism exhibit lower activity during the first few days of the storage period. The second case study displays drastic changes in specific erythrocyte metabolite pools at different times during storage at different temperatures. CONCLUSIONS: The new visualization technique GEM-Vis introduced in this article constitutes a well-suitable approach for large-scale network exploration and advances hypothesis generation. This method can be applied to any system with data and a metabolic map to promote visualization and understand physiology at the network level. More broadly, we hope that our approach will provide the blueprints for new visualizations of other longitudinal -omics data types. The supplement includes a comprehensive user's guide and links to a series of tutorial videos that explain how to prepare model and data files, and how to use the software SBMLsimulator in combination with further tools to create similar animations as highlighted in the case studies.

Improving sensitivity in microchip electrophoresis coupled to ESI-MS/MS on the example of a cardiac drug mixture.

A comprehensive study for a sensitivity optimization in MCE with mass spectrometric detection is presented. As a text mixture, we chose a mixture of the cardiac drugs propranolol, bisoprolol, lidocaine, procaine and studied the effect of different chip layouts and experimental parameters with the aim of achieving both high sensitivity in MS detection and adequate chip electrophoretic separation. An important aspect was a comparison of microfluidic layouts containing various sheath-flow channels with that avoiding sheath-flow junctions on-chip. We utilized glass chips with monolithically integrated nanospray emitter tips coupled dead volume-free to an IT mass spectrometer running in fragmentation mode (MS(n) ). With this setup, detection limits down to 0.6 ng/mL for the model compound propranolol were achieved.