Sustainable Hues: Exploring the Molecular Palette of Biowaste Dyes through LC-MS Metabolomics
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ABSTRACT:
SUBMITTER: Molino R
PROVIDER: S-EPMC8587104 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlzheimer's Disease (AD) is a complex and multifactorial disease and novel approaches are needed to illuminate the underlying pathology. Metabolites comprise the end-product of genes, transcripts, and protein regulations and might reflect disease pathogenesis. Blood is a common biofluid used in metabolomics; however, since extracellular vesicles (EVs) hold cell-specific biological material and can cross the blood-brain barrier, their utilization as biological material warrants further investigation. We aimed to investigate blood- and EV-derived metabolites to add insigts to the pathological mechanisms of AD.MethodsBlood samples were collected from 10 AD and 10 Mild Cognitive Impairment (MCI) patients, and 10 healthy controls. EVs were enriched from plasma using 100,000×g, 1 h, 4 °C with a wash. Metabolites from serum and EVs were measured using liquid chromatography-mass spectrometry (LC-MS) and nuclear magnetic resonance (NMR) spectroscopy. Multivariate and univariate analyses were employed to identify altered metabolites in cognitively impaired individuals.ResultsWhile no significant EV-derived metabolites were found differentiating patients from healthy individuals, six serum metabolites were found important; valine (p = 0.001, fold change, FC = 0.8), histidine (p = 0.001, FC = 0.9), allopurinol riboside (p = 0.002, FC = 0.2), inosine (p = 0.002, FC = 0.3), 4-pyridoxic acid (p = 0.006, FC = 1.6), and guanosine (p = 0.004, FC = 0.3). Pathway analysis revealed branched-chain amino acids, purine and histidine metabolisms to be downregulated, and vitamin B6 metabolism upregulated in patients compared to controls.ConclusionUsing a combination of LC-MS and NMR methodologies we identified several altered mechanisms possibly related to AD pathology. EVs require additional optimization prior to their possible utilization as a biological material for AD-related metabolomics studies.
| S-EPMC8479251 | biostudies-literature
Project description:BackgroundUntargeted metabolomics datasets contain large proportions of uninformative features that can impede subsequent statistical analysis such as biomarker discovery and metabolic pathway analysis. Thus, there is a need for versatile and data-adaptive methods for filtering data prior to investigating the underlying biological phenomena. Here, we propose a data-adaptive pipeline for filtering metabolomics data that are generated by liquid chromatography-mass spectrometry (LC-MS) platforms. Our data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients.ResultsUsing metabolomics datasets that were generated in our laboratory from samples of human blood, as well as two public LC-MS datasets, we compared our data-adaptive filtering method with traditional methods that rely on non-method specific thresholds. The data-adaptive approach outperformed traditional approaches in terms of removing noisy features and retaining high quality, biologically informative ones. The R code for running the data-adaptive filtering method is provided at https://github.com/courtneyschiffman/Metabolomics-Filtering .ConclusionsOur proposed data-adaptive filtering pipeline is intuitive and effectively removes uninformative features from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.
| S-EPMC6570933 | biostudies-literature
Project description:Optical recording of membrane potential permits spatially resolved measurement of electrical activity in subcellular regions of single cells, which would be inaccessible to electrodes, and imaging of spatiotemporal patterns of action potential propagation in excitable tissues, such as the brain or heart. However, the available voltage-sensitive dyes (VSDs) are not always spectrally compatible with newly available optical technologies for sensing or manipulating the physiological state of a system. Here, we describe a series of 19 fluorinated VSDs based on the hemicyanine class of chromophores. Strategic placement of the fluorine atoms on the chromophores can result in either blue or red shifts in the absorbance and emission spectra. The range of one-photon excitation wavelengths afforded by these new VSDs spans 440-670 nm; the two-photon excitation range is 900-1,340 nm. The emission of each VSD is shifted by at least 100 nm to the red of its one-photon excitation spectrum. The set of VSDs, thus, affords an extended toolkit for optical recording to match a broad range of experimental requirements. We show the sensitivity to voltage and the photostability of the new VSDs in a series of experimental preparations ranging in scale from single dendritic spines to whole heart. Among the advances shown in these applications are simultaneous recording of voltage and calcium in single dendritic spines and optical electrophysiology recordings using two-photon excitation above 1,100 nm.
| S-EPMC3528613 | biostudies-literature
Project description:Untargeted metabolomics using high-resolution liquid chromatography-mass spectrometry (LC-MS) is becoming one of the major areas of high-throughput biology. Functional analysis, that is, analyzing the data based on metabolic pathways or the genome-scale metabolic network, is critical in feature selection and interpretation of metabolomics data. One of the main challenges in the functional analyses is the lack of the feature identity in the LC-MS data itself. By matching mass-to-charge ratio (m/z) values of the features to theoretical values derived from known metabolites, some features can be matched to one or more known metabolites. When multiple matchings occur, in most cases only one of the matchings can be true. At the same time, some known metabolites are missing in the measurements. Current network/pathway analysis methods ignore the uncertainty in metabolite identification and the missing observations, which could lead to errors in the selection of significant subnetworks/pathways. In this paper, we propose a flexible network feature selection framework that combines metabolomics data with the genome-scale metabolic network. The method adopts a sequential feature screening procedure and machine learning-based criteria to select important subnetworks and identify the optimal feature matching simultaneously. Simulation studies show that the proposed method has a much higher sensitivity than the commonly used maximal matching approach. For demonstration, we apply the method on a cohort of healthy subjects to detect subnetworks associated with the body mass index (BMI). The method identifies several subnetworks that are supported by the current literature, as well as detects some subnetworks with plausible new functional implications. The R code is available at http://web1.sph.emory.edu/users/tyu8/MSS.
| S-EPMC5441461 | biostudies-literature
Project description:IntroductionPancreatic ductal adenocarcinoma (PDAC) is the fifth most common cause of cancer-related death in Europe with a 5-year survival rate of <5%. Chronic pancreatitis (CP) is a risk factor for PDAC development, but in the majority of cases malignancy is discovered too late for curative treatment. There is at present no reliable diagnostic marker for PDAC available.ObjectivesThe aim of the study was to identify single blood-based metabolites or a panel of metabolites discriminating PDAC and CP using liquid chromatography-mass spectrometry (LC-MS).MethodsA discovery cohort comprising PDAC (n = 44) and CP (n = 23) samples was analyzed by LC-MS followed by univariate (Student's t test) and multivariate (orthogonal partial least squares-discriminant analysis (OPLS-DA)) statistics. Discriminative metabolite features were subject to raw data examination and identification to ensure high feature quality. Their discriminatory power was then confirmed in an independent validation cohort including PDAC (n = 20) and CP (n = 31) samples.ResultsGlycocholic acid, N-palmitoyl glutamic acid and hexanoylcarnitine were identified as single markers discriminating PDAC and CP by univariate analysis. OPLS-DA resulted in a panel of five metabolites including the aforementioned three metabolites as well as phenylacetylglutamine (PAGN) and chenodeoxyglycocholate.ConclusionUsing LC-MS-based metabolomics we identified three single metabolites and a five-metabolite panel discriminating PDAC and CP in two independent cohorts. Although further study is needed in larger cohorts, the metabolites identified are potentially of use in PDAC diagnostics.
| S-EPMC5376388 | biostudies-literature
Project description:MotivationWhen metabolites are analyzed by electrospray ionization (ESI)-mass spectrometry, they are usually detected as multiple ion species due to the presence of isotopes, adducts and in-source fragments. The signals generated by these degenerate features (along with contaminants and other chemical noise) obscure meaningful patterns in MS data, complicating both compound identification and downstream statistical analysis. To address this problem, we developed Binner, a new tool for the discovery and elimination of many degenerate feature signals typically present in untargeted ESI-LC-MS metabolomics data.ResultsBinner generates feature annotations and provides tools to help users visualize informative feature relationships that can further elucidate the underlying structure of the data. To demonstrate the utility of Binner and to evaluate its performance, we analyzed data from reversed phase LC-MS and hydrophilic interaction chromatography (HILIC) platforms and demonstrated the accuracy of selected annotations using MS/MS. When we compared Binner annotations of 75 compounds previously identified in human plasma samples with annotations generated by three similar tools, we found that Binner achieves superior performance in the number and accuracy of annotations while simultaneously minimizing the number of incorrectly annotated principal ions. Data reduction and pattern exploration with Binner have allowed us to catalog a number of previously unrecognized complex adducts and neutral losses generated during the ionization of molecules in LC-MS. In summary, Binner allows users to explore patterns in their data and to efficiently and accurately eliminate a significant number of the degenerate features typically found in various LC-MS modalities.Availability and implementationBinner is written in Java and is freely available from http://binner.med.umich.edu.Supplementary informationSupplementary data are available at Bioinformatics online.
| S-EPMC7828469 | biostudies-literature
Project description:Untargeted metabolomics can detect more than 10 000 peaks in a single LC-MS run. The correspondence between these peaks and metabolites, however, remains unclear. Here, we introduce a Peak Annotation and Verification Engine (PAVE) for annotating untargeted microbial metabolomics data. The workflow involves growing cells in 13C and 15N isotope-labeled media to identify peaks from biological compounds and their carbon and nitrogen atom counts. Improved deisotoping and deadducting are enabled by algorithms that integrate positive mode, negative mode, and labeling data. To distinguish metabolites and their fragments, PAVE experimentally measures the response of each peak to weak in-source collision induced dissociation, which increases the peak intensity for fragments while decreasing it for their parent ions. The molecular formulas of the putative metabolites are then assigned based on database searching using both m/ z and C/N atom counts. Application of this procedure to Saccharomyces cerevisiae and Escherichia coli revealed that more than 80% of peaks do not label, i.e., are environmental contaminants. More than 70% of the biological peaks are isotopic variants, adducts, fragments, or mass spectrometry artifacts yielding ∼2000 apparent metabolites across the two organisms. About 650 match to a known metabolite formula based on m/ z and C/N atom counts, with 220 assigned structures based on MS/MS and/or retention time to match to authenticated standards. Thus, PAVE enables systematic annotation of LC-MS metabolomics data with only ∼4% of peaks annotated as apparent metabolites.
| S-EPMC6501219 | biostudies-literature
Project description:Metabolism plays a fundamental role in regulating cellular functions and fate decisions. Liquid chromatography-mass spectrometry (LC-MS)-based targeted metabolomic approaches provide high-resolution insights into the metabolic state of a cell. However, the typical sample size is in the order of 105-107 cells and thus not compatible with rare cell populations, especially in the case of a prior flow cytometry-based purification step. Here, we present a comprehensively optimized protocol for targeted metabolomics on rare cell types, such as hematopoietic stem cells and mast cells. Only 5000 cells per sample are required to detect up to 80 metabolites above background. The use of regular-flow liquid chromatography allows for robust data acquisition, and the omission of drying or chemical derivatization avoids potential sources of error. Cell-type-specific differences are preserved while the addition of internal standards, generation of relevant background control samples, and targeted metabolite with quantifiers and qualifiers ensure high data quality. This protocol could help numerous studies to gain thorough insights into cellular metabolic profiles and simultaneously reduce the number of laboratory animals and the time-consuming and costly experiments associated with rare cell-type purification.
| S-EPMC9996616 | biostudies-literature
Project description:Liquid chromatography coupled to mass spectrometry (LC-MS) is commonly applied to investigate the small molecule complement of organisms. Several software tools are typically joined in custom pipelines to semi-automatically process and analyse the resulting data. General workflow environments like the Konstanz Information Miner (KNIME) offer the potential of an all-in-one solution to process LC-MS data by allowing easy integration of different tools and scripts. We describe MassCascade and its workflow plug-in for processing LC-MS data. The Java library integrates frequently used algorithms in a modular fashion, thus enabling it to serve as back-end for graphical front-ends. The functions available in MassCascade have been encapsulated in a plug-in for the workflow environment KNIME, allowing combined use with e.g. statistical workflow nodes from other providers and making the tool intuitive to use without knowledge of programming. The design of the software guarantees a high level of modularity where processing functions can be quickly replaced or concatenated. MassCascade is an open-source library for LC-MS data processing in metabolomics. It embraces the concept of visual programming through its KNIME plug-in, simplifying the process of building complex workflows. The library was validated using open data.
| S-EPMC4524413 | biostudies-literature
Project description:The management of large amounts of eggshell waste annually produced in the world is problematic as generally this material is only disposed at landfills with odor production and microbial growth. On the contrary, significant environmental and economic advantages could be obtained transforming this biowaste into new value-added products. Eggshell biowaste was the starting material for the synthesis of hydroxyapatite by a simple and sustainable procedure and applied for the removal of Co2+ from aqueous solutions. The effects of contact time and initial metal concentration were investigated in batch experiments. Eggshell-based hydroxyapatite (ESHAP) before and after Co2+ removal was characterized by X-ray diffraction and scanning electron microscopy. The process was rapid and reached equilibrium within 80?min. The removal efficiency was in the range 70-80% which is generally higher than other waste-derived adsorbents. Adsorption of Co2+ on the surface of ESHAP particles and ion exchange with Ca2+ resulting in the formation of a Co-phosphate are the main mechanisms of the metal removal. The conversion of eggshell waste to a low-cost adsorbent for the treatment of metal contaminated waters could contribute to a more sustainable and effective management of this biowaste.
| S-EPMC7016106 | biostudies-literature