An untargeted mass spectrometry analysis of a biological sample will detect both biological molecules and compounds that are derived from, for example, diet and the environment. This Review examines the design of such experiments, how to process and interpret the vast amount of data that are produced, and how far we are from being able to use mass spectrometry to inventory the world around us.
Keywords: Mass spectrometry, MetabolomicsThe rise of “omics sciences”, with high-throughput measurements of cellular macromolecules DNA, RNA and proteins, has opened up avenues to the measurement of cellular small organic molecules, which is the foundation of metabolomics. The metabolome is defined as the complete set of small organic molecules produced by a given cell in a given time and space. Metabolomics is therefore defined as the set of analytical techniques used to measure a large subset of the metabolome. In this chapter we focus on the mass spectrometry (MS) platforms applied to metabolomics, under the assumption that no single analytical platform is capable of measuring all of the metabolome. The main MS-based metabolomics approaches are contextualized to molecular classes and metabolic partition targeted in experiment, and a guide for experimental design is explored. Experimental design includes the most recent analytical and computational resources that point towards the possible factors that influence the analysis and, consequently, the results. We seek to enable metabolomics practitioners to correctly design experiments, based on specific biological questions, and to keep in mind which workflow is best suited to the study goal for the metabolites being sampled. In addition, we discuss several issues surrounding the analytical platform and the main MS parameters for acquiring metabolomics data, as well as the application of quality control, and finally the statistical analysis from data. The main goal of metabolomics is the understanding of phenotypical changes through unbiased data analysis interpretation. To achieve this goal, an integrated approach from experimental design to data processing is required.
Journal of The American Society for Mass Spectrometry, 30(2):268--277, feb 2019.
Polymers are a common component of chemical background which complicates data analysis and can impair interpretation. Undesired chemical background cannot always be addressed via pre-analytical methods, chromatography, or existing data processing methods. The Kendrick mass filter (KMF) is presented for the computational removal of undesired signals present in MS1 spectra. The KMF is also able to provide a high-level view of the compositionality of data regarding the presence of signals with repeat units and indicate the presence of different polymers.
BMC Biology, 17(1):47, dec 2019.
Use of skin personal care products on a regular basis is nearly ubiquitous, but their effects on molecular and microbial diversity of the skin are unknown. We evaluated the impact of four beauty products (a facial lotion, a moisturizer, a foot powder, and a deodorant) on 11 volunteers over 9 weeks. Mass spectrometry and 16S rRNA inventories of the skin revealed decreases in chemical as well as in bacterial and archaeal diversity on halting deodorant use. Specific compounds from beauty products used before the study remain detectable with half-lives of 0.5–1.9 weeks. The deodorant and foot powder increased molecular, bacterial, and archaeal diversity, while arm and face lotions had little effect on bacterial and archaeal but increased chemical diversity. Personal care product effects last for weeks and produce highly individualized responses, including alterations in steroid and pheromone levels and in bacterial and archaeal ecosystem structure and dynamics. These findings may lead to next-generation precision beauty products and therapies for skin disorders.
Keywords: Life Sciences, generalPLOS Computational Biology, 14(4):e1006089, apr 2018.
The annotation of small molecules is one of the most challenging and important steps in untargeted mass spectrometry analysis, as most of our biological interpretations rely on structural annotations. Molecular networking has emerged as a structured way to organize and mine data from untargeted tandem mass spectrometry (MS/MS) experiments and has been widely applied to propagate annotations. However, propagation is done through manual inspection of MS/MS spectra connected in the spectral networks and is only possible when a reference library spectrum is available. One of the alternative approaches used to annotate an unknown fragmentation mass spectrum is through the use of in silico predictions. One of the challenges of in silico annotation is the uncertainty around the correct structure among the predicted candidate lists. Here we show how molecular networking can be used to improve the accuracy of in silico predictions through propagation of structural annotations, even when there is no match to a MS/MS spectrum in spectral libraries. This is accomplished through creating a network consensus of re-ranked structural candidates using the molecular network topology and structural similarity to improve in silico annotations. The Network Annotation Propagation (NAP) tool is accessible through the GNPS web-platform https://gnps.ucsd.edu/ProteoSAFe/static/gnps-theoretical.jsp.
Proceedings of the National Academy of Sciences, page 201516878, oct 2015.
Extract Despite the over 100-y history of mass spectrometry, it remains challenging to link the large volume of known chemical structures to the data obtained with mass spectrometers. Presently, only 1.8% of spectra in an untargeted metabolomics experiment can be annotated. This means that the vast majority of information collected by metabolomics is “dark matter,” chemical signatures that remain uncharacterized (Fig. 1). For a genomic comparison, 80% of predicted genes in the Escherichia coli genome are known. In a bacteriophage metagenome, a well-known frontier of biological dark matter, the amount of known genes is 1–30%, depending on the sample (1). Thus, one could argue that we know more about the genetics of uncultured phage than we do about the chemistry within our own bodies. Much of the chemical dark matter may include known structures, but they remain undiscovered because the reference spectra are not available in mass spectrometry databases. The only way to overcome this challenge is through the development of computational solutions. In PNAS, Dührkop et al. describe the development of such a computational tool, called CSI (compound structure identification):FingerID (2). The tool is designed to aid in the annotation of chemistries that can be observed by mass spectrometry. CSI:FingerID uses fragmentation trees to connect tandem MS (MS/MS) data to chemical structures found in public chemistry databases. Tools such as this can allow metabolomics with mass spectrometry to become as commonly used and scientifically productive as sequencing technologies have in the field of genomics.
Bioinformatics (Oxford, England), 30(9):1336--1337, feb 2014.
SUMMARY: We present ProbMetab, an R package that promotes substantial improvement in automatic probabilistic liquid chromatography-mass spectrometry-based metabolome annotation. The inference engine core is based on a Bayesian model implemented to (i) allow diverse source of experimental data and metadata to be systematically incorporated into the model with alternative ways to calculate the likelihood function and (ii) allow sensitive selection of biologically meaningful biochemical reaction databases as Dirichlet-categorical prior distribution. Additionally, to ensure result interpretation by system biologists, we display the annotation in a network where observed mass peaks are connected if their candidate metabolites are substrate/product of known biochemical reactions. This graph can be overlaid with other graph-based analysis, such as partial correlation networks, in a visualization scheme exported to Cytoscape, with web and stand-alone versions.Availability and implementation: ProbMetab was implemented in a modular manner to fit together with established upstream (xcms, CAMERA, AStream, mzMatch.R, etc) and downstream R package tools (GeneNet, RCytoscape, DiffCorr, etc). ProbMetab, along with extensive documentation and case studies, is freely available under GNU license at: http://labpib.fmrp.usp.br/methods/probmetab/.