Comprehensive Tandem-Mass-Spectrometry Coverage of Complex Samples Enabled by Data-Set-Dependent Acquisition.

Tandem mass spectrometry (MS/MS) is an invaluable experimental tool for providing analytical data supporting the identification of small molecules and peptides in mass-spectrometry-based "omics" experiments. Data-dependent MS/MS (DDA) is a real-time MS/MS-acquisition strategy that is responsive to the signals detected in a given sample. However, in analysis of even moderately complex samples with state-of-the-art instrumentation, the speed of MS/MS acquisition is insufficient to offer comprehensive MS/MS coverage of all detected molecules. Data-independent approaches (DIA) offer greater MS/MS coverage, typically at the expense of selectivity or sensitivity. This report describes data-set-dependent MS/MS (DsDA), a novel integration of MS1-data processing and target prioritization to enable comprehensive MS/MS sampling during the initial MS-level experiment. This approach is guided by the premise that in omics experiments, individual injections are typically made as part of a larger set of samples, and feedback between data processing and data acquisition can allow approximately real-time optimization of MS/MS-acquisition parameters and nearly complete MS/MS-sampling coverage. Using a combination of R, Proteowizard, XCMS, and WRENS software, this concept was implemented on a liquid-chromatograph-coupled quadrupole time-of-flight mass spectrometer. The results illustrate comprehensive MS/MS coverage for a set of complex small-molecule samples and demonstrate a strong improvement on traditional DDA.

Stacked Injections of Biphasic Extractions for Improved Metabolomic Coverage and Sample Throughput.

Omics technologies attempt to provide comprehensive coverage of their target analytes. Comprehensive coverage of metabolites, the aim of nontargeted metabolomics applications, is hindered by the extreme diversity in physiochemical properties of the metabolome. One approach to deal with this challenge is the use of biphasic extractions. These methods generate two largely complementary extracts from a single sample, with an organic lipid-rich fraction and an aqueous fraction containing largely primary and secondary metabolites. To improve metabolite coverage, these two fractions are then independently analyzed resulting in a doubling of the experimental time. In this manuscript, we describe a novel injection approach, stacked injections of a biphasic extraction (SIBE), which enables simultaneous analysis of the two fractions. We demonstrate that SIBE offers nearly 3-fold more total peak area than a monophasic extract without dramatically increasing instrumentation time required for the analysis. The analytical variance is very slightly increased; however, significant improvements in retention time stability are obtained with SIBE vs monophasic injections. Collectively, these data indicate that SIBE is a viable injection approach whenever comprehensive metabolomic coverage is desired.

Leveraging non-targeted metabolite profiling via statistical genomics.

One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.

Rice bran fermented with saccharomyces boulardii generates novel metabolite profiles with bioactivity.

Emerging evidence supporting chronic disease fighting properties of rice bran has advanced the development of stabilized rice bran for human use as a functional food and dietary supplement. A global and targeted metabolomic investigation of stabilized rice bran fermented with Saccharomyces boulardii was performed in three rice varieties. Metabolites from S. boulardii-fermented rice bran were detected by gas chromatography-mass spectrometry (GC-MS) and assessed for bioactivity compared to nonfermented rice bran in normal and malignant lymphocytes. Global metabolite profiling revealed significant differences in the metabolome that led to discovery of candidate compounds modulated by S. boulardii fermentation. Fermented rice bran extracts from three rice varieties reduced growth of human B lymphomas compared to each variety's nonfermented control and revealed that fermentation differentially altered bioactive compounds. These data support that integration of global and targeted metabolite analysis can be utilized for assessing health properties of rice bran phytochemicals that are enhanced by yeast fermentation and that differ across rice varieties.