Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem.

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype.

A Metabolites Merging Strategy (MMS): Harmonization to Enable Studies' Intercomparison.

Metabolomics encounters challenges in cross-study comparisons due to diverse metabolite nomenclature and reporting practices. To bridge this gap, we introduce the Metabolites Merging Strategy (MMS), offering a systematic framework to harmonize multiple metabolite datasets for enhanced interstudy comparability. MMS has three steps.

The untargeted urine volatilome for biomedical applications: methodology and volatilome database.

Chemically diverse in compounds, urine can give us an insight into metabolic breakdown products from foods, drinks, drugs, environmental contaminants, endogenous waste metabolites, and bacterial by-products. Hundreds of them are volatile compounds; however, their composition has never been provided in detail, nor has the methodology used for urine volatilome untargeted analysis. Here, we summarize key elements for the untargeted analysis of urine volatilome from a comprehensive compilation of literature, including the latest reports published.

Urine NMR Metabolomics for Precision Oncology in Colorectal Cancer.

Metabolomics is a fundamental approach to discovering novel biomarkers and their potential use for precision medicine. When applied for population screening, NMR-based metabolomics can become a powerful clinical tool in precision oncology. Urine tests can be more widely accepted due to their intrinsic non-invasiveness.

Amanida: an R package for meta-analysis of metabolomics non-integral data.

Summary: The combination, analysis and evaluation of different studies which try to answer or solve the same scientific question, also known as a meta-analysis, plays a crucial role in answering relevant clinical relevant questions. Unfortunately, metabolomics studies rarely disclose all the statistical information needed to perform a meta-analysis. Here, we present a meta-analysis approach using only the most reported statistical parameters in this field: P-value and fold-change.

Comprehensive Volatilome and Metabolome Signatures of Colorectal Cancer in Urine: A Systematic Review and Meta-Analysis.

To increase compliance with colorectal cancer screening programs and to reduce the recommended screening age, cheaper and easy non-invasiveness alternatives to the fecal immunochemical test should be provided. Following the PRISMA procedure of studies that evaluated the metabolome and volatilome signatures of colorectal cancer in human urine samples, an exhaustive search in PubMed, Web of Science, and Scopus found 28 studies that met the required criteria. There were no restrictions on the query for the type of study, leading to not only colorectal cancer samples versus control comparison but also polyps versus control and prospective studies of surgical effects, CRC staging and comparisons of CRC with other cancers.

rMSIproc: an R package for mass spectrometry imaging data processing.

Summary: Mass spectrometry imaging (MSI) can reveal biochemical information directly from a tissue section. MSI generates a large quantity of complex spectral data which is still challenging to translate into relevant biochemical information. Here, we present rMSIproc, an open-source R package that implements a full data processing workflow for MSI experiments performed using TOF or FT-based mass spectrometers.

Assessing the potential of sputtered gold nanolayers in mass spectrometry imaging for metabolomics applications.

Mass spectrometry imaging (MSI) is a molecular imaging technique that maps the distribution of molecules in biological tissues with high spatial resolution. The most widely used MSI modality is matrix-assisted laser desorption/ionization (MALDI), mainly due to the large variety of analyte classes amenable for MALDI analysis. However, the organic matrices used in classical MALDI may impact the quality of the molecular images due to limited lateral resolution and strong background noise in the low mass range, hindering its use in metabolomics.

Novel automated workflow for spectral alignment and mass calibration in MS imaging using a sputtered Ag nanolayer.

Mass spectrometry imaging (MSI) is a technique that can map analyte spatial distribution directly onto a tissue section. This enables the spatial correlation of molecular entities with a tissue morphology to be investigated. Analyte annotation in MSI is intrinsically linked to the mass accuracy of the data.

rMSI: an R package for MS imaging data handling and visualization.

Summary: R platform provides some packages that are useful to process mass spectrometry imaging (MSI) data; however, none of them provide an easy to use graphical user interface (GUI). Here, we introduce rMSI, an R package for MSI data analysis focused on providing an efficient way to manage MSI data together with a GUI integrated in R environment. MS data is loaded in rMSI custom format optimized to minimize the memory footprint yet maintaining a fast spectra access.

Signal preprocessing, multivariate analysis and software tools for MA(LDI)-TOF mass spectrometry imaging for biological applications.

Mass spectrometry imaging (MSI) is a label-free analytical technique capable of molecularly characterizing biological samples, including tissues and cell lines. The constant development of analytical instrumentation and strategies over the previous decade makes MSI a key tool in clinical research. Nevertheless, most MSI studies are limited to targeted analysis or the mere visualization of a few molecular species (proteins, peptides, metabolites, or lipids) in a region of interest without fully exploiting the possibilities inherent in the MSI technique, such as tissue classification and segmentation or the identification of relevant biomarkers from an untargeted approach.

Avoiding hard chromatographic segmentation: A moving window approach for the automated resolution of gas chromatography-mass spectrometry-based metabolomics signals by multivariate methods.

Gas chromatography-mass spectrometry (GC-MS) produces large and complex datasets characterized by co-eluted compounds and at trace levels, and with a distinct compound ion-redundancy as a result of the high fragmentation by the electron impact ionization. Compounds in GC-MS can be resolved by taking advantage of the multivariate nature of GC-MS data by applying multivariate resolution methods. However, multivariate methods have to be applied in small regions of the chromatogram, and therefore chromatograms are segmented prior to the application of the algorithms.

eRah: A Computational Tool Integrating Spectral Deconvolution and Alignment with Quantification and Identification of Metabolites in GC/MS-Based Metabolomics.

Gas chromatography coupled to mass spectrometry (GC/MS) has been a long-standing approach used for identifying small molecules due to the highly reproducible ionization process of electron impact ionization (EI). However, the use of GC-EI MS in untargeted metabolomics produces large and complex data sets characterized by coeluting compounds and extensive fragmentation of molecular ions caused by the hard electron ionization. In order to identify and extract quantitative information on metabolites across multiple biological samples, integrated computational workflows for data processing are needed.

Automated resolution of chromatographic signals by independent component analysis-orthogonal signal deconvolution in comprehensive gas chromatography/mass spectrometry-based metabolomics.

Comprehensive gas chromatography-mass spectrometry (GC×GC-MS) provides a different perspective in metabolomics profiling of samples. However, algorithms for GC×GC-MS data processing are needed in order to automatically process the data and extract the purest information about the compounds appearing in complex biological samples. This study shows the capability of independent component analysis-orthogonal signal deconvolution (ICA-OSD), an algorithm based on blind source separation and distributed in an R package called osd, to extract the spectra of the compounds appearing in GC×GC-MS chromatograms in an automated manner.

Performance Comparison of Fuzzy ARTMAP and LDA in Qualitative Classification of Iranian Rosa damascena Essential Oils by an Electronic Nose.

Quality control of essential oils is an important topic in industrial processing of medicinal and aromatic plants. In this paper, the performance of Fuzzy Adaptive Resonant Theory Map (ARTMAP) and linear discriminant analysis (LDA) algorithms are compared in the specific task of quality classification of Rosa damascene essential oil samples (one of the most famous and valuable essential oils in the world) using an electronic nose (EN) system based on seven metal oxide semiconductor (MOS) sensors. First, with the aid of a GC-MS analysis, samples of Rosa damascene essential oils were classified into three different categories (low, middle, and high quality, classes C1, C2, and C3, respectively) based on the total percent of the most crucial qualitative compounds.

Compound identification in gas chromatography/mass spectrometry-based metabolomics by blind source separation.

Metabolomics GC-MS samples involve high complexity data that must be effectively resolved to produce chemically meaningful results. Multivariate curve resolution-alternating least squares (MCR-ALS) is the most frequently reported technique for that purpose. More recently, independent component analysis (ICA) has been reported as an alternative to MCR.

Dolphin: a tool for automatic targeted metabolite profiling using 1D and 2D (1)H-NMR data.

One of the main challenges in nuclear magnetic resonance (NMR) metabolomics is to obtain valuable metabolic information from large datasets of raw NMR spectra in a high throughput, automatic, and reproducible way. To date, established software packages used to match and quantify metabolites in NMR spectra remain mostly manually operated, leading to low resolution results and subject to inconsistencies not attributable to the NMR technique itself. Here, we introduce a new software package, called Dolphin, able to automatically quantify a set of target metabolites in multiple sample measurements using an approach based on 1D and 2D NMR techniques to overcome the inherent limitations of 1D (1)H-NMR spectra in metabolomics.

Metabolomic assessment of the effect of dietary cholesterol in the progressive development of fatty liver disease.

Nonalcoholic fatty liver disease is considered to be the hepatic manifestation of metabolic syndrome and is usually related to high-fat, high-cholesterol diets. With the rationale that the identification and quantification of metabolites in different metabolic pathways may facilitate the discovery of clinically accessible biomarkers, we report the use of (1)H NMR metabolomics for quantitative profiling of liver extracts from LDLr(-/-) mice, a well-documented mouse model of fatty liver disease. A total of 55 metabolites were identified, and multivariate analyses in a diet- and time-comparative strategy were performed.

Metabolic phenotyping of genetically modified mice: An NMR metabonomic approach.

Monocyte chemoattractant protein-1 (MCP-1) plays a relevant role in macrophage migration but recent findings suggest an additional role in lipid and glucose metabolism. We report the use of (1)H NMR spectroscopy as a useful complementary method to assess the metabolic function of this gene in a comparative strategy. This metabonomic analysis was rapid, simple, quantitative and reproducible, and revealed a suggestive relationship between the expression of the MCP-1 gene and hepatic glucose and taurine concentrations.

Selectivity Enhancement in Multisensor Systems Using Flow Modulation Techniques.

In this paper, the use of a new technique to obtain transient sensor information is introduced and its usefulness to improve the selectivity of metal oxide gas sensors is discussed. The method is based on modulating the flow of the carrier gas that brings the species to be measured into the sensor chamber. In such a way, the analytes' concentration at the surface of the sensors is altered.

Early detection of fungal growth in bakery products by use of an electronic nose based on mass spectrometry.

This paper presents the design, optimization, and evaluation of a mass spectrometry-based electronic nose (MS e-nose) for early detection of unwanted fungal growth in bakery products. Seven fungal species (Aspergillus flavus, Aspergillus niger, Eurotium amstelodami, Eurotium herbariorum, Eurotium rubrum, Eurotium repens, and Penicillium corylophillum) were isolated from bakery products and used for the study. Two sampling headspace techniques were tested: static headspace (SH) and solid-phase microextraction (SPME).