CAMERA: An Integrated Strategy for Compound Spectra Extraction and Annotation of Liquid Chromatography/Mass Spectrometry Data Sets

TLDR

Liquid chromatography–mass spectrometry is widely used for metabolomics, but compound annotation remains a manual bottleneck. The authors present CAMERA, a Bioconductor package that extracts compound spectra, annotates isotope and adduct peaks, and proposes accurate compound masses in complex data. CAMERA integrates algorithms for spectrum extraction, isotope and adduct annotation, and accurate mass prediction, and its performance was evaluated against manual annotations of spiked mixtures and enhanced by combining data from opposite ion modes. CAMERA achieved 89.7% and 90.3% accurate mass extraction in positive and negative ion modes, and its application in two plant metabolomics studies markedly reduced manual analysis.

Abstract

Liquid chromatography coupled to mass spectrometry is routinely used for metabolomics experiments. In contrast to the fairly routine and automated data acquisition steps, subsequent compound annotation and identification require extensive manual analysis and thus form a major bottleneck in data interpretation. Here we present CAMERA, a Bioconductor package integrating algorithms to extract compound spectra, annotate isotope and adduct peaks, and propose the accurate compound mass even in highly complex data. To evaluate the algorithms, we compared the annotation of CAMERA against a manually defined annotation for a mixture of known compounds spiked into a complex matrix at different concentrations. CAMERA successfully extracted accurate masses for 89.7% and 90.3% of the annotatable compounds in positive and negative ion modes, respectively. Furthermore, we present a novel annotation approach that combines spectral information of data acquired in opposite ion modes to further improve the annotation rate. We demonstrate the utility of CAMERA in two different, easily adoptable plant metabolomics experiments, where the application of CAMERA drastically reduced the amount of manual analysis.