An IROA Workflow for correction and normalization of ion suppression in mass spectrometry-based metabolomic profiling data.

Ion suppression is a major problem in mass spectrometry (MS)-based metabolomics; it can dramatically decrease measurement accuracy, precision, and signal-to-noise sensitivity. Here we report a new method, the IROA TruQuant Workflow, that uses a stable isotope-labeled internal standard (IROA-IS) plus novel companion algorithms to 1) measure and correct for ion suppression, and 2) perform Dual MSTUS normalization of MS metabolomic data. We have evaluated the method across ion chromatography (IC), hydrophilic interaction liquid chromatography (HILIC), and reverse phase liquid chromatography (RPLC)-MS systems in both positive and negative ionization modes, with clean and unclean ion sources, and across different biological matrices. Across the broad range of conditions tested, all detected metabolites exhibited ion suppression ranging from 1% to 90+% and coefficient of variations ranging from 1% to 20%, but the Workflow and companion algorithms were highly effective at nulling out that suppression and error. Overall, the Workflow corrects ion suppression across diverse analytical conditions and produces robust normalization of non-targeted metabolomic data.

An Isotopic Ratio Outlier Analysis Approach for Global Metabolomics of Biosynthetically Talented Actinomycetes.

Actinomycetes are powerhouses of natural product biosynthesis. Full realization of this biosynthetic potential requires approaches for recognizing novel metabolites and determining mediators of metabolite production. Herein, we develop an isotopic ratio outlier analysis (IROA) ultra-high performance liquid chromatography-mass spectrometry (UHPLC/MS) global metabolomics strategy for actinomycetes that facilitates recognition of novel metabolites and evaluation of production mediators. We demonstrate this approach by determining impacts of the iron chelator 2,2'-bipyridyl on the Nocardiopsis dassonvillei metabolome. Experimental and control cultures produced metabolites with isotopic carbon signatures that were distinct from corresponding "standard" culture metabolites, which were used as internal standards for LC/MS. This provided an isotopic MS peak pair for each metabolite, which revealed the number of carbon atoms and relative concentrations of metabolites and distinguished biosynthetic products from artifacts. Principal component analysis (PCA) and random forest (RF) differentiated bipyridyl-treated samples from controls. RF mean decrease accuracy (MDA) values supported perturbation of metabolites from multiple amino acid pathways and novel natural products. Evaluation of bipyridyl impacts on the nocazine/XR334 diketopiperazine (DKP) pathway revealed upregulation of amino acid precursors and downregulation of late stage intermediates and products. These results establish IROA as a tool in the actinomycete natural product chemistry arsenal and support broad metabolic consequences of bipyridyl.

Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis.

There is always a tension within the omics sciences between trying to measure biological molecules rapidly and measuring accurately. Metabolomics as an omics science tries to measure the small biochemicals rapidly, in a single pass, but the current state of the art cannot provide the reproducibility or accuracy needed for clinical use or even daily reproducibility for larger experiments. The IROA TruQuant measurement system uses a daily "Long-Term Reference Standard (LTRS)" and a chemically identical Internal Standard (IS) to provide validated chemical identity, daily QA/QC on instrument and sample preparation, and accurate reproducible quantitation that is comparable across days, instruments, and even, for most compounds, chromatographic methods. The LTRS is, as the name implies, a Long-Term Reference Standard that is always the same and should therefore provide very similar results on a large but finite collection of compounds. All of the compounds in the LTRS are isotopically signed with formula indicating IROA patterns so they cannot be mistaken for one another. Because of the precise IROA patterns, a software-driven analysis of the compounds seen daily can determine the performance of the instrument in terms of sensitivity, in-source fragmentation, and chromatographic and injection stability and provide completely reproducible quantitation.

Quantitative Metabolomics Using Isotope Residue Outlier Analysis (IROA®) with Internal Standards.

Various research strategies involving biomarker discovery and mechanistic studies in system biology depend on reproducible and reliable quantification of all metabolites from tissue(s) of interest. Contemporary analytical methods rely on mass spectrometry-based targeted and/or untargeted metabolomics platforms. The robustness of these analyses depends on the cleanliness of the samples, accuracy of the database, resolution of the instrument, and, the most variable of the list, the personal preferences of the researcher and the instrument operator. In this chapter, we introduce a simple method to prepare murine liver samples and carry it through the Isotope Ratio Outlier Analysis (IROA®) pipeline. This pipeline encompasses sample preparation, LC-MS-based peak acquisition, proprietary software-based library creation, normalization, and quantification of metabolites. IROA® offers a unique platform to create and normalize a local library and account for run-to-run variability over years of acquisition using the internal standards (IROA®-IS) and long-term reference standards (IROA®-LTRS).

Isotopic ratio outlier analysis improves metabolomics prediction of nitrogen treatment in maize.

We evaluated Isotope Ratio Outlier Analysis (IROA) as a metabolome-wide internal standard approach to improve the quality of LC/MS data collected from a large-scale greenhouse experiment designed to metric the ability of metabolomics to model quantitatively nitrogen treatments. We further looked at how IROA would be incorporated into a metabolomics workflow. For this we compared IROA processed data with that generated without the benefit of metabolome-wide internal standards using our current tool, Genedata Expressionist, from the same raw LC/MS data files. In our experiment, 367 maize plants were grown from kernel in a greenhouse under controlled conditions. Plants were treated from germination on with varying concentrations of nutrient nitrogen as one (treatment) variable. A second variable was the presence of one of two transgenes. Metabolomics analysis of leaves was performed by LC/MS positive and negative electrospray ionization modes, and raw data were processed with both our routine and IROA protocols. IROA data analysis detected 184 metabolites in each ionization mode. Analysis without IROA yielded 281 metabolites in positive ionization mode and 172 in negative ionization mode. Data from both protocols were normalized for sample dry weight, location in the greenhouse, extraction batch, sample run order, and internal standard. Normalized results were subjected to partial least squares (PLS) analysis to model the relationship between the metabolome and nitrogen treatment. Without IROA, regression coefficients of 0.819 and 0.849 for positive and negative modes, respectively were achieved. The IROA protocol improved on the values, yielding regression coefficients of 0.876 and 0.879 for positive and negative modes, respectively. In addition, IROA corrected for detector saturation for several high abundant peaks. Our experiment demonstrates that incorporating IROA into an LC/MS metabolomics experiment improves data quality and facilitates more precise modeling of a biological response.

Addressing the current bottlenecks of metabolomics: Isotopic Ratio Outlier Analysis™, an isotopic-labeling technique for accurate biochemical profiling.

Metabolomics or biochemical profiling is a fast emerging science; however, there are still many associated bottlenecks to overcome before measurements will be considered robust. Advances in MS resolution and sensitivity, ultra pressure LC-MS, ESI, and isotopic approaches such as flux analysis and stable-isotope dilution, have made it easier to quantitate biochemicals. The digitization of mass spectrometers has simplified informatic aspects. However, issues of analytical variability, ion suppression and metabolite identification still plague metabolomics investigators. These hurdles need to be overcome for accurate metabolite quantitation not only for in vitro systems, but for complex matrices such as biofluids and tissues, before it is possible to routinely identify biomarkers that are associated with the early prediction and diagnosis of diseases. In this report, we describe a novel isotopic-labeling method that uses the creation of distinct biochemical signatures to eliminate current bottlenecks and enable accurate metabolic profiling.