Abstract
We present a novel data analysis strategy which combined with subcellular fractionation and liquid chromatography–mass spectrometry (LC-MS) based proteomics provides a simple and effective workflow for global drug profiling. Five subcellular fractions were obtained by differential centrifugation followed by high resolution LC-MS and complete functional regulation analysis. The methodology combines functional regulation and enrichment analysis into a single visual summary. The workflow enables improved insight into perturbations caused by drugs. We provide a statistical argument to demonstrate that even crude subcellular fractions leads to improved functional characterization. We demonstrate this data analysis strategy on data obtained in a MS-based global drug profiling study. However, this strategy can also be performed on other types of large scale biological data.
Highlights
Employed approaches for global drug profiling include methods based on epigenomics by generation sequencing[1], transcriptomics using either microarrays or generation sequencing[2] and mass spectrometry for profiling proteins[3] and metabolites[4]
We here demonstrate that a standard subcellular fractionation method[15], combined with liquid chromatography–mass spectrometry (LC-MS) followed by a novel “Complete Functional Regulation Analysis” provides an effective and powerful technology for gaining functional insight into drug effects
MS data for the Insoluble Nuclear and MiCrossomal (MC) crude fractions were obtained for the analysis presented using fractions obtained simultaneously with our previous study
Summary
Employed approaches for global drug profiling include methods based on epigenomics by generation sequencing[1], transcriptomics using either microarrays or generation sequencing[2] and mass spectrometry for profiling proteins[3] and metabolites[4]. In general significant regulated proteins are defined by applying filters to log ratios and P values followed by functional enrichment analysis using tools such as bioinformatics server DAVID12 (i.e. Individual Entity Analysis, see Fig. 1A). Such methods are sensitive to the applied P value and log ratio thresholds. Different statistical methods for functional analysis of large scale biological data based on the statistical strategies, outlined in Fig. 1A,B, have been reviewed by Nam et al.[13] These statistical methods were developed for technologies that collect gene data such as e.g. microarray platforms. “Complete Functional Regulation Analysis” condenses the statistical significant results into a single heatmap for each type of functional annotation (e.g. cellular component, biological process, molecular function, KEGG, etc)
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