Non-targeted metabolomics screen comparing metabolite profiles of serum from PDAC-bearing mice that received metronidazole using high-resolution, high-performance LC-MS/MS analysis.
Project description:BackgroundLiquid chromatography-mass spectrometry (LC-MS) utilizing the high-resolution power of an orbitrap is an important analytical technique for both metabolomics and proteomics. Most important feature of the orbitrap is excellent mass accuracy. Thus, it is necessary to convert raw data to accurate and reliable m/z values for metabolic fingerprinting by high-resolution LC-MS.ResultsIn the present study, we developed a novel, easy-to-use and straightforward m/z detection method, AMDORAP. For assessing the performance, we used real biological samples, Bacillus subtilis strains 168 and MGB874, in the positive mode by LC-orbitrap. For 14 identified compounds by measuring the authentic compounds, we compared obtained m/z values with other LC-MS processing tools. The errors by AMDORAP were distributed within ±3 ppm and showed the best performance in m/z value accuracy.ConclusionsOur method can detect m/z values of biological samples much more accurately than other LC-MS analysis tools. AMDORAP allows us to address the relationships between biological effects and cellular metabolites based on accurate m/z values. Obtaining the accurate m/z values from raw data should be indispensable as a starting point for comparative LC-orbitrap analysis. AMDORAP is freely available under an open-source license at http://amdorap.sourceforge.net/.
Project description:High resolution mass spectrometry (HR-MS) offers the opportunity to track large numbers of non-target analytes through water treatment processes, providing a more comprehensive view of reactor performance than targeted evaluation. Both approaches were used to evaluate the performance of a pilot scale advanced oxidation process (AOP) employing ultraviolet light and hydrogen peroxide (UV/H2O2) to treat municipal wastewater effluent. Twelve pharmaceuticals and personal care products were selected as target compounds and added to reactor influent. Target compound removal over a range of flow rates and hydrogen peroxide addition levels was assessed using a liquid chromatograph combined with a quadrupole time-of-flight mass spectrometer (LC-qTOF-MS). Target compound removals were used to determine hydroxyl radical concentrations and UV fluence under pilot scale conditions. The experiments were also analyzed using a nontarget approach, which identified "molecular features" in either reactor influent or effluent. Strong correlation (r = 0.94) was observed between target compound removals calculated using the targeted and non-targeted approaches across the range of reactor conditions tested. The two approaches also produced consistent rankings of the performance of the various reactor operating conditions, although the distribution of compound removal efficiencies was usually less favorable with the broader, nontarget approach. For example, in the UV only treatment 8.3% of target compounds and 2.2% of non-target compounds exhibited removals above 50%, while 100% of target compounds and 74% of non-target compounds exhibited removals above 50% in the best condition tested. These results suggest that HR-MS methods can provide more holistic evaluation of reactor performance, and may reduce biases caused by selection of a limited number of target compounds. HR-MS methods also offer insights into the composition of poorly removed compounds and the formation of transformation products, which were widely detected.
Project description:BackgroundLiquid chromatography coupled to mass spectrometry (LC/MS) is an important analytical technology for e.g. metabolomics experiments. Determining the boundaries, centres and intensities of the two-dimensional signals in the LC/MS raw data is called feature detection. For the subsequent analysis of complex samples such as plant extracts, which may contain hundreds of compounds, corresponding to thousands of features -- a reliable feature detection is mandatory.ResultsWe developed a new feature detection algorithm centWave for high-resolution LC/MS data sets, which collects regions of interest (partial mass traces) in the raw-data, and applies continuous wavelet transformation and optionally Gauss-fitting in the chromatographic domain. We evaluated our feature detection algorithm on dilution series and mixtures of seed and leaf extracts, and estimated recall, precision and F-score of seed and leaf specific features in two experiments of different complexity.ConclusionThe new feature detection algorithm meets the requirements of current metabolomics experiments. centWave can detect close-by and partially overlapping features and has the highest overall recall and precision values compared to the other algorithms, matchedFilter (the original algorithm of XCMS) and the centroidPicker from MZmine. The centWave algorithm was integrated into the Bioconductor R-package XCMS and is available from (http://www.bioconductor.org/).
Project description:Chemical database searching has become a fixture in many non-targeted identification workflows based on high-resolution mass spectrometry (HRMS). However, the form of a chemical structure observed in HRMS does not always match the form stored in a database (e.g., the neutral form versus a salt; one component of a mixture rather than the mixture form used in a consumer product). Linking the form of a structure observed via HRMS to its related form(s) within a database will enable the return of all relevant variants of a structure, as well as the related metadata, in a single query. A Konstanz Information Miner (KNIME) workflow has been developed to produce structural representations observed using HRMS ("MS-Ready structures") and links them to those stored in a database. These MS-Ready structures, and associated mappings to the full chemical representations, are surfaced via the US EPA's Chemistry Dashboard ( https://comptox.epa.gov/dashboard/ ). This article describes the workflow for the generation and linking of ~ 700,000 MS-Ready structures (derived from ~ 760,000 original structures) as well as download, search and export capabilities to serve structure identification using HRMS. The importance of this form of structural representation for HRMS is demonstrated with several examples, including integration with the in silico fragmentation software application MetFrag. The structures, search, download and export functionality are all available through the CompTox Chemistry Dashboard, while the MetFrag implementation can be viewed at https://msbi.ipb-halle.de/MetFragBeta/ .
Project description:Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics in order to provide coherent and high-quality data that enable discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce notame, an analytical workflow for non-targeted metabolic profiling approaches, utilizing liquid chromatography-mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data and, finally, to identify and interpret the compounds that have emerged as interesting.
Project description:DNA can be damaged through covalent modifications of the nucleobases by endogenous processes. These modifications, commonly referred to as DNA adducts, can persist and may lead to mutations, and ultimately to the initiation of cancer. A screening methodology for the majority of known endogenous DNA adducts would be a powerful tool for investigating the etiology of cancer and for the identification of individuals at high-risk to the detrimental effects of DNA damage. This idea led to the development of a DNA adductomic approach using high resolution data-dependent scanning, an extensive MS2 fragmentation inclusion list of known endogenous adducts, and neutral loss MS3 triggering to profile all DNA modifications. In this method, the detection of endogenous DNA adducts is performed by observation of their corresponding MS3 neutral loss triggered events and their relative quantitation using the corresponding full scan extracted ion chromatograms. The method's inclusion list consists of the majority of known endogenous DNA adducts, compiled, and reported here, as well as adducts specific to tobacco exposure included to compare the performance of the method with previously developed targeted approaches. The sensitivity of the method was maximized by reduction of extraneous background signal through the purification and minimization of the amount of commercially obtained enzymes used for the DNA hydrolysis. In addition, post-hydrolysis sample purification was performed using off-line HPLC fraction collection to eliminate the highly abundant unmodified bases, and to avoid introduction of plasticizers found in solid-phase extraction cartridges. Also, several instrument parameters were evaluated to optimize the ion signal intensities and fragmentation spectra quality. The method was tested on an animal model of lung carcinogenesis where A/J mice were exposed to the tobacco specific lung carcinogen 4-methylnitrosamino-1-3-pyridyl-1-butanone (NNK) with its effects enhanced by co-exposure to the pro-inflammatory agent lipopolysaccharide (LPS). Lung DNA were screened for endogenous DNA adducts known to result from oxidative stress and LPS-induced lipid peroxidation, as well as for adducts due to NNK exposure. The relative quantitation of the detected DNA adducts was performed using parallel reaction monitoring MS2 analysis, demonstrating a general workflow for analysis of endogenous DNA adducts.
Project description:Background: Testosterone is an androgenic hormone that plays important roles in both males and females. The circulating levels of total testosterone vary from 1 to 1480 ng/dL. High-throughput immunoassays often lack accuracy in lower concentration ranges (below 100 ng/dL), particularly when used for females or children. To address this limitation, we developed a total testosterone LC-MS/MS assay on three instruments. Methods: Sample preparation began with the dilution and conditioning of 200 µL of serum. A supported liquid extraction cartridge was used to extract the analyte from biological matrices. Chromatographic separation was achieved using a C18 column with a runtime of 5 min per sample. This assay was validated on a Triple Quad 6500 and an API 4500 instrument. Results: Method validation was carried out according to the CLSI C62-ED2 guideline and our hospital protocol. The within-day coefficient of variation (CV) was less than 10% and the between-day CV was less than 15%. The assay had a limit of quantitation of 0.5 ng/dL with an analyte measure range of 2-1200 ng/dL. A comparison using Deming regression and Bland-Altman plots showed that this assay correlated well with a reference method. The results from the API 4500 and an Orbitrap were consistent with those from the TQ 6500. Both serum-separator tubes (BD) and serum-activator tubes were found to be suitable. Conclusions: We successfully developed and validated a robust total testosterone LC-MS/MS assay for routine clinical testing. This assay was harmonized across two triple quadrupole instruments and one high-resolution mass spectrometer.
Project description:Tibetan donkeys inhabit the harsh environment of the Qinghai-Tibet Plateau. Research on serum metabolites related to their high-altitude adaptation is limited compared to other livestock. We used liquid chromatography-mass spectrometry (LC-MS) to analyze serum samples from healthy adult donkeys in Shigatse, Changdu, and Dezhou to evaluate the effects of high altitudes on serum metabolites. Metabolomics analysis identified 443 differential metabolites (DMs) across the three groups, meeting criteria of VIP ≥ 1, p-value < 0.05, and Fold-Change ≥ 1.2 or ≤ 0.5. Significant upregulation was observed in deoxycholic acid, vitamin A, vitamin C, flavin mononucleotide, n-acetylserotonin, N'-formyl-kynurenine, calcidiol, and adenosine monophosphate in the high-altitude group compared to the low-altitude control group. The DMs were involved in processes such as bile secretion, vitamin digestion and absorption, tryptophan metabolism, and parathyroid hormone synthesis, secretion, and action. All these are crucial for nutrient metabolism, immune function, and antioxidant stress response. This study constructed a metabolomics dataset of Tibetan donkey serum and revealed differential metabolites among donkeys from different geographic regions and environments. The results offer crucial insights into the adaptive regulatory mechanisms.
Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.