Hybrid Feature Detection and Information Accumulation Using High-Resolution LC–MS Metabolomics Data

Feature detection is a critical step in the preprocessing of liquid chromatography–mass spectrometry (LC–MS) metabolomics data. Currently, the predominant approach is to detect features using noise filters and peak shape models based on the data at hand alone. Databases of known metabolites and historical data contain information that could help boost the sensitivity of feature detection, especially for low-concentration metabolites. However, utilizing such information in targeted feature detection may cause large number of false positives because of the high levels of noise in LC–MS data. With high-resolution mass spectrometry such as liquid chromatograph–Fourier transform mass spectrometry (LC–FTMS), high-confidence matching of peaks to known features is feasible. Here we describe a computational approach that serves two purposes. First it boosts feature detection sensitivity by using a hybrid procedure of both untargeted and targeted peak detection. New algorithms are designed to reduce the chance of false-positives by nonparametric local peak detection and filtering. Second, it can accumulate information on the concentration variation of metabolites over large number of samples, which can help find rare features and/or features with uncommon concentration in future studies. Information can be accumulated on features that are consistently found in real data even before their identities are found. We demonstrate the value of the approach in a proof-of-concept study. The method is implemented as part of the R package apLCMS at http://www.sph.emory.edu/apLCMS/

Detailed Investigation and Comparison of the XCMS and MZmine 2 Chromatogram Construction and Chromatographic Peak Detection Methods for Preprocessing Mass Spectrometry Metabolomics Data

XCMS and MZmine 2 are two widely used software packages for preprocessing untargeted LC/MS metabolomics data. Both construct extracted ion chromatograms (EICs) and detect peaks from the EICs, the first two steps in the data preprocessing workflow. While both packages have performed admirably in peak picking, they also detect a problematic number of false positive EIC peaks and can also fail to detect real EIC peaks. The former and latter translate downstream into spurious and missing compounds and present significant limitations with most existing software packages that preprocess untargeted mass spectrometry metabolomics data. We seek to understand the specific reasons why XCMS and MZmine 2 find the false positive EIC peaks that they do and in what ways they fail to detect real compounds. We investigate differences of EIC construction methods in XCMS and MZmine 2 and find several problems in the XCMS <i>centWave</i> peak detection algorithm which we show are partly responsible for the false positive and false negative compound identifications. In addition, we find a problem with MZmine 2’s use of <i>centWave</i>. We hope that a detailed understanding of the XCMS and MZmine 2 algorithms will allow users to work with them more effectively and will also help with future algorithmic development

One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks

False positive and false negative peaks detected from extracted ion chromatograms (EIC) are an urgent problem with existing software packages that preprocess untargeted liquid or gas chromatography–mass spectrometry metabolomics data because they can translate downstream into spurious or missing compound identifications. We have developed new algorithms that carry out the sequential construction of EICs and detection of EIC peaks. We compare the new algorithms to two popular software packages XCMS and MZmine 2 and present evidence that these new algorithms detect significantly fewer false positives. Regarding the detection of compounds known to be present in the data, the new algorithms perform at least as well as XCMS and MZmine 2. Furthermore, we present evidence that mass tolerance in <i>m</i>/<i>z</i> should be favored rather than mass tolerance in ppm in the process of constructing EICs. The mass tolerance parameter plays a critical role in the EIC construction process and can have immense impact on the detection of EIC peaks

A breakdown of the animals used for mitochondrial isolation and metabolic profiling studies.

<p>A total of 40 animals were used, 20 wild type and 20 thiroredoxin-2 transgenic (Trx2TG). These groups were further broken down into subgroups of 5 based on age and sex.</p

Distribution of metabolites resolved by anion exchange (A) and reverse phase (B) chromatography.

<p>To determine if a metabolite was present in the mitochondrial isolation and not a buffer contaminant, a ratio of ion intensity (sample ion intensity/buffer ion intensity) was calculated for each metabolite. A metabolite was determined to be “mitochondrial” if this ratio (s/b) was greater than or equal to 4. Additionally, employment of two chromatographic techniques resulted in the detection of 2127 mitochondrial metabolites (C).</p

Mitochondrial metabolites from C18 that were found to discriminate male from female mitochondria using false discovery rate analysis (q = 0.1).

<p>Metabolites with greater ion intensity in male mitochondria include (A) leucine/isoleucine, (B) glutamate and (C) methionine. Metabolites with greater ion intensity in female mitochondria include (D) adenosine, (E) sphinganine and (F) unknown metabolite (<i>m/z</i> 442.759). Data was analyzed using one-way ANOVA and Tukey's post hoc test (* p<0.05, *** p<0.001).</p

Most metabolic pathways are represented in metabolites isolated from liver mitochondria.

<p>Ten pathways containing the most matches are plotted.</p

Mitochondrial metabolites from AE that were found to discriminate male from female mitochondria using false discovery rate analysis (q = 0.1).

<p>Metabolites with greater ion intensity in male mitochondria include (A) leucine/isoleucine, (B) glutamate and (C) methionine. Metabolites with greater ion intensity in female mitochondria include (D) adenosine, (E) amino octadecanoic acid and (F) unknown metabolite (<i>m/z</i> 537.789). Data was analyzed using one-way ANOVA and Tukey's post hoc test (* p<0.05, ** p<0.01, *** p<0.001).</p

Predicting Network Activity from High Throughput Metabolomics

<div><p>The functional interpretation of high throughput metabolomics by mass spectrometry is hindered by the identification of metabolites, a tedious and challenging task. We present a set of computational algorithms which, by leveraging the collective power of metabolic pathways and networks, predict functional activity directly from spectral feature tables without a priori identification of metabolites. The algorithms were experimentally validated on the activation of innate immune cells.</p></div

Identification of metabolites by tandem mass spectrometry.

<p>Arginine is shown as an example while the full data are given in Figure S3. From top to bottom: the fragmentation pattern ( followed by on peak 157) from biological sample, from biological sample spiked with authentic chemical and from authentic chemical reference.</p