High-Fat Diets Alter the Modulatory Effects of Xenobiotics on Cytochrome P450 Activities.

Cytochrome P450 monooxygenase (P450) enzymes metabolize critical endogenous chemicals and oxidize nearly all xenobiotics. Dysregulated P450 activities lead to altered capacity for drug metabolism and cellular stress. The effects of mixed exposures on P450 expression and activity are variable and elusive. A high-fat diet (HFD) is a common exposure that results in obesity and associated pathologies including hepatotoxicity. Herein, we report the effects of cigarette smoke on P450 activities of normal weight and HFD induced obese mice. Activity-based protein profiling results indicate that HFD mice had significantly decreased P450 activity, likely instigated by proinflammatory chemicals, and that P450 enzymes involved in detoxification, xenobiotic metabolism, and bile acid synthesis were effected by HFD and smoke interaction. Smoking increased activity of all lung P450 and coexposure to diet effected P450 2s1. We need to expand our understanding of common exposures coupled to altered P450 metabolism to enhance the safety and efficacy of therapeutic drug dosing.

Susceptibility to quantum dot induced lung inflammation differs widely among the Collaborative Cross founder mouse strains.

Quantum dots (QDs) are engineered semiconductor nanoparticles with unique physicochemical properties that make them potentially useful in clinical, research and industrial settings. However, a growing body of evidence indicates that like other engineered nanomaterials, QDs have the potential to be respiratory hazards, especially in the context of the manufacture of QDs and products containing them, as well as exposures to consumers using these products. The overall goal of this study was to investigate the role of mouse strain in determining susceptibility to QD-induced pulmonary inflammation and toxicity. Male mice from 8 genetically diverse inbred strains (the Collaborative Cross founder strains) were exposed to CdSe-ZnS core-shell QDs stabilized with an amphiphilic polymer. QD treatment resulted in significant increases in the percentage of neutrophils and levels of cytokines present in bronchoalveolar lavage fluid (BALF) obtained from NOD/ShiLtJ and NZO/HlLtJ mice relative to their saline (Sal) treated controls. Cadmium measurements in lung tissue indicated strain-dependent differences in disposition of QDs in the lung. Total glutathione levels in lung tissue were significantly correlated with percent neutrophils in BALF as well as with lung tissue Cd levels. Our findings indicate that QD-induced acute lung inflammation is mouse strain dependent, that it is heritable, and that the choice of mouse strain is an important consideration in planning QD toxicity studies. These data also suggest that formal genetic analyses using additional strains or recombinant inbred strains from these mice could be useful for discovering potential QD-induced inflammation susceptibility loci.

Review, evaluation, and discussion of the challenges of missing value imputation for mass spectrometry-based label-free global proteomics.

In this review, we apply selected imputation strategies to label-free liquid chromatography-mass spectrometry (LC-MS) proteomics datasets to evaluate the accuracy with respect to metrics of variance and classification. We evaluate several commonly used imputation approaches for individual merits and discuss the caveats of each approach with respect to the example LC-MS proteomics data. In general, local similarity-based approaches, such as the regularized expectation maximization and least-squares adaptive algorithms, yield the best overall performances with respect to metrics of accuracy and robustness. However, no single algorithm consistently outperforms the remaining approaches, and in some cases, performing classification without imputation sometimes yielded the most accurate classification. Thus, because of the complex mechanisms of missing data in proteomics, which also vary from peptide to protein, no individual method is a single solution for imputation. On the basis of the observations in this review, the goal for imputation in the field of computational proteomics should be to develop new approaches that work generically for this data type and new strategies to guide users in the selection of the best imputation for their dataset and analysis objectives.

Bayesian proteoform modeling improves protein quantification of global proteomic measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that, with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian Proteoform Quantification model (BP-Quant)(1) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern or the existence of multiple overexpressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab® and R packages.

Comparative iron oxide nanoparticle cellular dosimetry and response in mice by the inhalation and liquid cell culture exposure routes.

BACKGROUND: Toxicity testing the rapidly growing number of nanomaterials requires large scale use of in vitro systems under the presumption that these systems are sufficiently predictive or descriptive of responses in in vivo systems for effective use in hazard ranking. We hypothesized that improved relationships between in vitro and in vivo models of experimental toxicology for nanomaterials would result from placing response data in vitro and in vivo on the same dose scale, the amount of material associated with cells. METHODS: Balb/c mice were exposed nose-only to an aerosol (68.6 nm CMD, 19.9 mg/m(3), 4 hours) generated from of 12.8 nm superparamagnetic iron oxide particles (SPIO). Target cell doses were calculated, histological evaluations conducted, and biomarkers of response were identified by global transcriptomics. Representative murine epithelial and macrophage cell types were exposed in vitro to the same material in liquid suspension for four hours and levels of nanoparticle regulated cytokine transcripts identified in vivo were quantified as a function of measured nanoparticle cellular dose. RESULTS: Target tissue doses of 0.009-0.4 µg SPIO/cm(2) in lung led to an inflammatory response in the alveolar region characterized by interstitial inflammation and macrophage infiltration. In vitro, higher target tissue doses of ~1.2-4 µg SPIO/ cm(2) of cells were required to induce transcriptional regulation of markers of inflammation, CXCL2 & CCL3, in C10 lung epithelial cells. Estimated in vivo macrophage SPIO nanoparticle doses ranged from 1-100 pg/cell, and induction of inflammatory markers was observed in vitro in macrophages at doses of 8-35 pg/cell. CONCLUSIONS: Application of target tissue dosimetry revealed good correspondence between target cell doses triggering inflammatory processes in vitro and in vivo in the alveolar macrophage population, but not in the epithelial cells of the alveolar region. These findings demonstrate the potential for target tissue dosimetry to enable the more quantitative comparison of in vitro and in vivo systems and advance their use for hazard assessment and extrapolation to humans. The mildly inflammogentic cellular doses experienced by mice were similar to those calculated for humans exposed to the same material at the existing permissible exposure limit of 10 mg/m(3) iron oxide (as Fe).

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements.

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.

A halotyrosine antibody that detects increased protein modifications in asthma patients.

Airway inflammation has a pathophysiological role in asthma. Eosinophils, which are commonly increased in asthmatic airways, express eosinophil peroxidase and thereby produce hypobromite and bromotyrosine. Bromotyrosine is believed to be a specific marker for eosinophil activity, but developing an antibody against monobromotyrosine, the predominant brominated tyrosine residue found in vivo has proven difficult. We evaluated whether a 3-bromobenozoic acid hapten antigen produced antibodies that recognized halogenated tyrosine residues. Studies with small-molecule inhibitors or brominated or chlorinated protein suggested that a mouse monoclonal antibody (BTK-94C) selectively bound free and protein mono- and dibromotyrosine and, to a lesser degree, chlorotyrosine, and thus was designated a general halotyrosine antibody. We evaluated if this antibody had potential for characterizing human asthma using an enzyme-linked immunosorbent assay (ELISA) microarray platform to examine the halogenation of 23 proteins in three independent sets of sputum samples (52 samples total). In 15 healthy control or asthmatic subjects, ICAM, PDGF and RANTES had greater proportional amounts of halogenation in asthmatic subjects and the halogenation signal was associated with the severity of exercise-induced airway hyperresponsiveness. In 17 severe asthma patients treated with placebo or mepolizumab to suppress eosinophils, drug-related decreases in halogenation were observed with p values ranging from 0.006 to 0.11 for these 3 proteins. Analysis of 20 subjects that either had neutrophilic asthma or were healthy controls demonstrated a broad increase in halotyrosine (possibly chlorotyrosine) in neutrophilic asthmatics. Overall, these results suggest that an ELISA utilizing BTK-94C could prove useful for assessing airway inflammation in asthma patients.

A semiautomated framework for integrating expert knowledge into disease marker identification.

BACKGROUND: The availability of large complex data sets generated by high throughput technologies has enabled the recent proliferation of disease biomarker studies. However, a recurring problem in deriving biological information from large data sets is how to best incorporate expert knowledge into the biomarker selection process. OBJECTIVE: To develop a generalizable framework that can incorporate expert knowledge into data-driven processes in a semiautomated way while providing a metric for optimization in a biomarker selection scheme. METHODS: The framework was implemented as a pipeline consisting of five components for the identification of signatures from integrated clustering (ISIC). Expert knowledge was integrated into the biomarker identification process using the combination of two distinct approaches; a distance-based clustering approach and an expert knowledge-driven functional selection. RESULTS: The utility of the developed framework ISIC was demonstrated on proteomics data from a study of chronic obstructive pulmonary disease (COPD). Biomarker candidates were identified in a mouse model using ISIC and validated in a study of a human cohort. CONCLUSIONS: Expert knowledge can be introduced into a biomarker discovery process in different ways to enhance the robustness of selected marker candidates. Developing strategies for extracting orthogonal and robust features from large data sets increases the chances of success in biomarker identification.

Dysregulation of macrophage activation profiles by engineered nanoparticles.

Although the potential human health impacts from exposure to engineered nanoparticles (ENPs) are uncertain, past epidemiological studies have established correlations between exposure to ambient air pollution particulates and the incidence of pneumonia and lung infections. Using amorphous silica and superparamagnetic iron oxide (SPIO) as model high production volume ENPs, we examined how macrophage activation by bacterial lipopolysaccharide (LPS) or the lung pathogen Streptococcus pneumoniae is altered by ENP pretreatment. Neither silica nor SPIO treatment elicited direct cytotoxic or pro-inflammatory effects in bone marrow-derived macrophages. However, pretreatment of macrophages with SPIO caused extensive reprogramming of nearly 500 genes regulated in response to LPS challenge, hallmarked by exaggerated activation of oxidative stress response pathways and suppressed activation of both pro- and anti-inflammatory pathways. Silica pretreatment altered regulation of only 67 genes, but there was strong correlation with gene sets affected by SPIO. Macrophages exposed to SPIO displayed a phenotype suggesting an impaired ability to transition from an M1 to M2-like activation state, characterized by suppressed IL-10 induction, enhanced TNFα production, and diminished phagocytic activity toward S. pneumoniae. Studies in macrophages deficient in scavenger receptor A (SR-A) showed SR-A participates in cell uptake of both the ENPs and S. pneumonia and co-regulates the anti-inflammatory IL-10 pathway. Thus, mechanisms for dysregulation of innate immunity exist by virtue that common receptor recognition pathways are used by some ENPs and pathogenic bacteria, although the extent of transcriptional reprogramming of macrophage function depends on the physicochemical properties of the ENP after internalization. Our results also illustrate that biological effects of ENPs may be indirectly manifested only after challenging normal cell function. Nanotoxicology screening strategies should therefore consider how exposure to these materials alters susceptibility to other environmental exposures.

Impaired transcriptional response of the murine heart to cigarette smoke in the setting of high fat diet and obesity.

Smoking and obesity are each well-established risk factors for cardiovascular heart disease, which together impose earlier onset and greater severity of disease. To identify early signaling events in the response of the heart to cigarette smoke exposure within the setting of obesity, we exposed normal weight and high fat diet-induced obese (DIO) C57BL/6 mice to repeated inhaled doses of mainstream (MS) or sidestream (SS) cigarette smoke administered over a two week period, monitoring effects on both cardiac and pulmonary transcriptomes. MS smoke (250 µg wet total particulate matter (WTPM)/L, 5 h/day) exposures elicited robust cellular and molecular inflammatory responses in the lung with 1466 differentially expressed pulmonary genes (p < 0.01) in normal weight animals and a much-attenuated response (463 genes) in the hearts of the same animals. In contrast, exposures to SS smoke (85 µg WTPM/L) with a CO concentration equivalent to that of MS smoke (~250 CO ppm) induced a weak pulmonary response (328 genes) but an extensive cardiac response (1590 genes). SS smoke and to a lesser extent MS smoke preferentially elicited hypoxia- and stress-responsive genes as well as genes predicting early changes of vascular smooth muscle and endothelium, precursors of cardiovascular disease. The most sensitive smoke-induced cardiac transcriptional changes of normal weight mice were largely absent in DIO mice after smoke exposure, while genes involved in fatty acid utilization were unaffected. At the same time, smoke exposure suppressed multiple proteome maintenance genes induced in the hearts of DIO mice. Together, these results underscore the sensitivity of the heart to SS smoke and reveal adaptive responses in healthy individuals that are absent in the setting of high fat diet and obesity.

Sequential projection pursuit principal component analysis--dealing with missing data associated with new -omics technologies.

Principal Component Analysis (PCA) is a common exploratory tool used to evaluate large complex data sets. The resulting lower-dimensional representations are often valuable for pattern visualization, clustering, or classification of the data. However, PCA cannot be applied directly to many -omics data sets generated by newer technologies such as label-free mass spectrometry due to large numbers of non-random missing values. Here we present a sequential projection pursuit PCA (sppPCA) method for defining principal components in the presence of missing data. Our results demonstrate that this approach generates robust and informative low-dimensional data representations compared to commonly used imputation approaches.

Diet-induced obesity reprograms the inflammatory response of the murine lung to inhaled endotoxin.

The co-occurrence of environmental factors is common in complex human diseases and, as such, understanding the molecular responses involved is essential to determine risk and susceptibility to disease. We have investigated the key biological pathways that define susceptibility for pulmonary infection during obesity in diet-induced obese (DIO) and regular weight (RW) C57BL/6 mice exposed to inhaled lipopolysaccharide (LPS). LPS induced a strong inflammatory response in all mice as indicated by elevated cell counts of macrophages and neutrophils and levels of proinflammatory cytokines (MDC, MIP-1γ, IL-12, RANTES) in the bronchoalveolar lavage fluid. Additionally, DIO mice exhibited 50% greater macrophage cell counts, but decreased levels of the cytokines, IL-6, TARC, TNF-α, and VEGF relative to RW mice. Microarray analysis of lung tissue showed over half of the LPS-induced expression in DIO mice consisted of genes unique for obese mice, suggesting that obesity reprograms how the lung responds to subsequent insult. In particular, we found that obese animals exposed to LPS have gene signatures showing increased inflammatory and oxidative stress response and decreased antioxidant capacity compared with RW. Because signaling pathways for these responses can be common to various sources of environmentally induced lung damage, we further identified biomarkers that are indicative of specific toxicant exposure by comparing gene signatures after LPS exposure to those from a parallel study with cigarette smoke. These data show obesity may increase sensitivity to further insult and that co-occurrence of environmental stressors result in complex biosignatures that are not predicted from analysis of individual exposures.

A comparative analysis of computational approaches to relative protein quantification using peptide peak intensities in label-free LC-MS proteomics experiments.

Liquid chromatography coupled with mass spectrometry (LC-MS) is widely used to identify and quantify peptides in complex biological samples. In particular, label-free shotgun proteomics is highly effective for the identification of peptides and subsequently obtaining a global protein profile of a sample. As a result, this approach is widely used for discovery studies. Typically, the objective of these discovery studies is to identify proteins that are affected by some condition of interest (e.g. disease, exposure). However, for complex biological samples, label-free LC-MS proteomics experiments measure peptides and do not directly yield protein quantities. Thus, protein quantification must be inferred from one or more measured peptides. In recent years, many computational approaches to relative protein quantification of label-free LC-MS data have been published. In this review, we examine the most commonly employed quantification approaches to relative protein abundance from peak intensity values, evaluate their individual merits, and discuss challenges in the use of the various computational approaches.

Proteomic analysis of bronchoalveolar lavage fluid proteins from mice infected with Francisella tularensis ssp. novicida.

Francisella tularensis causes the zoonosis tularemia in humans and is one of the most virulent bacterial pathogens. We utilized a global proteomic approach to characterize protein changes in bronchoalveolar lavage fluid from mice exposed to one of three organisms, F. tularensis ssp. novicida, an avirulent mutant of F. tularensis ssp. novicida (F.t. novicida-ΔmglA), and Pseudomonas aeruginosa. The composition of bronchoalveolar lavage fluid (BALF) proteins was altered following infection, including proteins involved in neutrophil activation, oxidative stress, and inflammatory responses. Components of the innate immune response were induced including the acute phase response and the complement system; however, the timing of their induction varied. F. tularensis ssp. novicida infected mice do not appear to have an effective innate immune response in the first hours of infection; however, within 24 h, they show an upregulation of innate immune response proteins. This delayed response is in contrast to P. aeruginosa infected animals which show an early innate immune response. Likewise, F.t. novicida-ΔmglA infection initiates an early innate immune response; however, this response is diminished by 24 h. Finally, this study identifies several candidate biomarkers, including Chitinase 3-like-1 (CHI3L1 or YKL-40) and peroxiredoxin 1, that are associated with F. tularensis ssp. novicida but not P. aeruginosa infection.

Electrochemical immunoassay of cotinine in serum based on nanoparticle probe and immunochromatographic strip.

A disposable sensor for the determination of cotinine in human serum was developed based on immunochromatographic test strip and quantum dot label. In this assay, cotinine linked with quantum dot competes with cotinine in sample to bind to anti-cotinine antibody in the test strip and the quantum dots serve as signal vehicles for electrochemical readout. Some parameters governing the performance of the sensor were optimized. The sensor shows a wide linear range from 1 ng mL(-1) to 100 ng mL(-1) cotinine with a detection limit of 1.0 ng mL(-1). The sensor was validated with spiked human serum samples and it was found that this method was reliable in measuring cotinine in human serum. The results demonstrate that this sensor is rapid, accurate, and less expensive and has the potential for point of care (POC) detection of cotinine and fast screening of tobacco smoke exposure.

Aerosolized ZnO nanoparticles induce toxicity in alveolar type II epithelial cells at the air-liquid interface.

The majority of in vitro studies characterizing the impact of engineered nanoparticles (NPs) on cells that line the respiratory tract were conducted in cells exposed to NPs in suspension. This approach introduces processes that are unlikely to occur during inhaled NP exposures in vivo, such as the shedding of toxic doses of dissolved ions. ZnO NPs are used extensively and pose significant sources for human exposure. Exposures to airborne ZnO NPs can induce adverse effects, but the relevance of the dissolved Zn(2+) to the observed effects in vivo is still unclear. Our goal was to mimic in vivo exposures to airborne NPs and decipher the contribution of the intact NP from the contribution of the dissolved ions to airborne ZnO NP toxicity. We established the exposure of alveolar type II epithelial cells to aerosolized NPs at the air-liquid interface (ALI) and compared the impact of aerosolized ZnO NPs and NPs in suspension at the same cellular doses, measured as the number of particles per cell. By evaluating membrane integrity and cell viability 6 and 24 h post-exposure, we found that aerosolized NPs induced toxicity at the ALI at doses that were in the same order of magnitude as doses required to induce toxicity in submersed cultures. In addition, distinct patterns of oxidative stress were observed in the two exposure systems. These observations unravel the ability of airborne ZnO NPs to induce toxicity without the contribution of dissolved Zn(2+) and suggest distinct mechanisms at the ALI and in submersed cultures.

A statistical selection strategy for normalization procedures in LC-MS proteomics experiments through dataset-dependent ranking of normalization scaling factors.

Quantification of LC-MS peak intensities assigned during peptide identification in a typical comparative proteomics experiment will deviate from run-to-run of the instrument due to both technical and biological variation. Thus, normalization of peak intensities across an LC-MS proteomics dataset is a fundamental step in pre-processing. However, the downstream analysis of LC-MS proteomics data can be dramatically affected by the normalization method selected. Current normalization procedures for LC-MS proteomics data are presented in the context of normalization values derived from subsets of the full collection of identified peptides. The distribution of these normalization values is unknown a priori. If they are not independent from the biological factors associated with the experiment the normalization process can introduce bias into the data, possibly affecting downstream statistical biomarker discovery. We present a novel approach to evaluate normalization strategies, which includes the peptide selection component associated with the derivation of normalization values. Our approach evaluates the effect of normalization on the between-group variance structure in order to identify the most appropriate normalization methods that improve the structure of the data without introducing bias into the normalized peak intensities.

t4 workshop report. Nanotoxicology: "the end of the beginning" - signs on the roadmap to a strategy for assuring the safe application and use of nanomaterials.

In October 2010, a group of experts met as part of the transatlantic think tank for toxicology (t4) to exchange ideas about the current status and future of safety testing of nanomaterials. At present, there is no widely accepted path forward to assure appropriate and effective hazard identification for engineered nanomaterials. The group discussed needs for characterization of nanomaterials and identified testing protocols that incorporate the use of innovative alternative whole models such as zebrafish or C. elegans, as well as in vitro or alternative methods to examine specific functional pathways and modes of action. The group proposed elements of a potential testing scheme for nanomaterials that works towards an integrated testing strategy, incorporating the goals of the NRC report Toxicity Testing in the 21st Century: A Vision and a Strategy by focusing on pathways of toxic response, and utilizing an evidence-based strategy for developing the knowledge base for safety assessment. Finally, the group recommended that a reliable, open, curated database be developed that interfaces with existing databases to enable sharing of information.

Quantitative proteomics analysis of adsorbed plasma proteins classifies nanoparticles with different surface properties and size.

Nanoparticle biological activity, biocompatibility and fate can be directly affected by layers of readily adsorbed host proteins in biofluids. Here, we report a study on the interactions between human blood plasma proteins and nanoparticles with a controlled systematic variation of properties using (18)O-labeling and LC-MS-based quantitative proteomics. We developed a novel protocol to both simplify isolation of nanoparticle bound proteins and improve reproducibility. LC-MS analysis identified and quantified 88 human plasma proteins associated with polystyrene nanoparticles consisting of three different surface chemistries and two sizes, as well as, for four different exposure times (for a total of 24 different samples). Quantitative comparison of relative protein abundances was achieved by spiking an (18)O-labeled "universal" reference into each individually processed unlabeled sample as an internal standard, enabling simultaneous application of both label-free and isotopic labeling quantification across the entire sample set. Clustering analysis of the quantitative proteomics data resulted in distinctive patterns that classified the nanoparticles based on their surface properties and size. In addition, temporal data indicated that the formation of the stable protein corona was at equilibrium within 5 min. The comprehensive quantitative proteomics results obtained in this study provide rich data for computational modeling and have potential implications towards predicting nanoparticle biocompatibility.

Improved quality control processing of peptide-centric LC-MS proteomics data.

MOTIVATION: In the analysis of differential peptide peak intensities (i.e. abundance measures), LC-MS analyses with poor quality peptide abundance data can bias downstream statistical analyses and hence the biological interpretation for an otherwise high-quality dataset. Although considerable effort has been placed on assuring the quality of the peptide identification with respect to spectral processing, to date quality assessment of the subsequent peptide abundance data matrix has been limited to a subjective visual inspection of run-by-run correlation or individual peptide components. Identifying statistical outliers is a critical step in the processing of proteomics data as many of the downstream statistical analyses [e.g. analysis of variance (ANOVA)] rely upon accurate estimates of sample variance, and their results are influenced by extreme values. RESULTS: We describe a novel multivariate statistical strategy for the identification of LC-MS runs with extreme peptide abundance distributions. Comparison with current method (run-by-run correlation) demonstrates a significantly better rate of identification of outlier runs by the multivariate strategy. Simulation studies also suggest that this strategy significantly outperforms correlation alone in the identification of statistically extreme liquid chromatography-mass spectrometry (LC-MS) runs. AVAILABILITY: https://www.biopilot.org/docs/Software/RMD.php CONTACT: bj@pnl.gov SUPPLEMENTARY INFORMATION: Supplementary material is available at Bioinformatics online.