Automated comparative metabolite profiling of large LC-ESIMS datasets in ACD/Labs, and data clustering on a new open-source web platform FreeClust

The technological development of LC-MS instrumentation has led to significant improvements of performance and sensitivity. Complex samples, such as plant extracts can now be analyzed in high-throughput mode. Software tools allow efficient deconvolution of LC-MS chromatograms to obtain comprehensive information on single constituents. However, the systematic and unbiased comparative metabolite profiling of large numbers of complex LC-MS chromatograms remains a challenge. Existing software tools for comparative processing have certain limitations, such as black-box approach, lack of user friendliness, or limited options for data sharing.

We developed a two-step protocol comprising a comparative metabolite profiling tool integrated in ACD/Labs, and a web platform developed in R language designed for clustering and visualization of chromatographic data. Initially, all relevant chromatographic and spectroscopic data (retention time, molecular ions with the respective ion abundance, and sample names) are automatically extracted and assembled in an Excel spreadsheet. Afterwards, the file is loaded into an online web application equipped with various statistical algorithms where the user can compare and visualize the results in intuitive 2D heat maps.

Here we applied this processing workflow to LC-ESIMS profiles obtained with 69 honey samples. Within few hours of calculation with a standard PC, the LC-ESIMS chromatograms were deconvoluted. Honey samples were organized in clusters based on their metabolite profile similarities, thereby highlighting the common metabolite patterns and distributions among samples. Implementation in the ACD/Labs software package enables ulterior integration of other analytical data and in silico prediction tools for modern drug discovery.