As the lipidomics field continues to advance, self-evaluation within the community is critical. Here, we performed an interlaboratory comparison exercise for lipidomics using Standard Reference Material (SRM) 1950-Metabolites in Frozen Human Plasma, a commercially available reference material. The interlaboratory study comprised 31 diverse laboratories, with each laboratory using a different lipidomics workflow. A total of 1,527 unique lipids were measured across all laboratories and consensus location estimates and associated uncertainties were determined for 339 of these lipids measured at the sum composition level by five or more participating laboratories. These evaluated lipids detected in SRM 1950 serve as community-wide benchmarks for intra- and interlaboratory quality control and method validation. These analyses were performed using nonstandardized laboratory-independent workflows. The consensus locations were also compared with a previous examination of SRM 1950 by the LIPID MAPS consortium. While the central theme of the interlaboratory study was to provide values to help harmonize lipids, lipid mediators, and precursor measurements across the community, it was also initiated to stimulate a discussion regarding areas in need of improvement.

Respiratory diseases are multifactorial heterogeneous diseases that have proved recalcitrant to understanding using focused molecular techniques. This trend has led to the rise of 'omics approaches (e.g., transcriptomics, proteomics) and subsequent acquisition of large-scale datasets consisting of multiple variables. In 'omics technology-based investigations, discrepancies between the number of variables analyzed (e.g., mRNA, proteins, metabolites) and the number of study subjects constitutes a major statistical challenge. The application of traditional univariate statistical methods (e.g., t-test) to these "short-and-wide" datasets may result in high numbers of false positives, while the predominant approach of p-value correction to account for these high false positive rates (e.g., FDR, Bonferroni) are associated with significant losses in statistical power. In other words, the benefit in decreased false positives must be counterbalanced with a concomitant loss in true positives. As an alternative, multivariate statistical analysis (MVA) is increasingly being employed to cope with 'omics-based data structures. When properly applied, MVA approaches can be powerful tools for integration and interpretation of complex 'omics-based datasets towards the goal of identifying biomarkers and/or subphenotypes. However, MVA methods are also prone to over-interpretation and misuse. A common software used in biomedical research to perform MVA-based analyses is the SIMCA package, which includes multiple MVA methods. In this opinion piece, we propose guidelines for minimum reporting standards for a SIMCA-based workflow, in terms of data preprocessing (e.g., normalization, scaling) and model statistics (number of components, R2, Q2, and CV-ANOVA p-value). Examples of these applications in recent COPD and asthma studies are provided. It is expected that readers will gain an increased understanding of the power and utility of MVA methods for applications in biomedical research.

Human blood is a self-regenerating lipid-rich biological fluid that is routinely collected in hospital settings. The inventory of lipid molecules found in blood plasma (plasma lipidome) offers insights into individual metabolism and physiology in health and disease. Disturbances in the plasma lipidome also occur in conditions that are not directly linked to lipid metabolism; therefore, plasma lipidomics based on MS is an emerging tool in an array of clinical diagnostics and disease management. However, challenges exist in the translation of such lipidomic data to clinical applications. These relate to the reproducibility, accuracy, and precision of lipid quantitation, study design, sample handling, and data sharing. This position paper emerged from a workshop that initiated a community-led process to elaborate and define a set of generally accepted guidelines for quantitative MS-based lipidomics of blood plasma or serum, with harmonization of data acquired on different instrumentation platforms across independent laboratories as an ultimate goal. We hope that other fields may benefit from and follow such a precedent.

Clustering based on clinicophysiologic parameters yielded 4 stable and reproducible clusters that associate with different pathobiological pathways.

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Inflammatory lung diseases are highly complex in respect of pathogenesis and relationships between inflammation, clinical disease and response to treatment. Sophisticated large-scale analytical methods to quantify gene expression (transcriptomics), proteins (proteomics), lipids (lipidomics) and metabolites (metabolomics) in the lungs, blood and urine are now available to identify biomarkers that define disease in terms of combined clinical, physiological and patho-biological abnormalities. The aspiration is that these approaches will improve diagnosis, i.e. define pathological phenotypes, and facilitate the monitoring of disease and therapy, and also, unravel underlying molecular pathways. Biomarker studies can either select predefined biomarker(s) measured by specific methods or apply an ''unbiased'' approach involving detection platforms that are indiscriminate in focus. This article reviews the technologies presently available to study biomarkers of lung disease within the 'omics field. The contributions of the individual 'omics analytical platforms to the field of respiratory diseases are summarised, with the goal of providing background on their respective abilities to contribute to systems medicine-based studies of lung disease. @ERSpublications Summary of the application of 'omics-based analytical platforms for biomarker discovery in inflammatory lung diseases

Carboxylesterases hydrolyze numerous endogenous and exogenous ester-containing compounds. They play a role in the detoxification of many agrochemicals including pyrethroids, organophosphates, and carbamates. Research on these enzymes is still developing and there are several topics that should be addressed to further investigations in this area. This paper focuses on a number of these issues including enzyme nomenclature, catalytic mechanism, substrate specificity, agrochemical metabolism, role in insecticide resistance and environmental significance. It is expected that carboxylesterase research will increase with specific emphasis on isozyme and substrate identification. Future research directions are discussed and the current state of the field is evaluated.