1H NMR spectra from HepG2 cells after treatment with Cyclosporin A
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ORGANISM(S): Homo Sapiens
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PROVIDER: S-DIXA-084 | biostudies-other |
REPOSITORIES: biostudies-other
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Project description: Not available
| S-DIXA-082 | biostudies-other
Project description:NMR spectroscopy continues to provide important molecular level details of dynamics in different polymer materials, ranging from rubbers to highly crosslinked composites. It has been argued that thermoset polymers containing dynamic and chemical heterogeneities can be fully cured at temperatures well below the final glass transition temperature (Tg). In this paper, we described the use of static solid-state 1H NMR spectroscopy to measure the activation of different chain dynamics as a function of temperature. Near Tg, increasing polymer segmental chain fluctuations lead to dynamic averaging of the local homonuclear proton-proton (1H-1H) dipolar couplings, as reflected in the reduction of the NMR line shape second moment (M2) when motions are faster than the magnitude of the dipolar coupling. In general, for polymer systems, distributions in the dynamic correlation times are commonly expected. To help identify the limitations and pitfalls of M2 analyses, the impact of activation energy or, equivalently, correlation time distributions, on the analysis of 1H NMR M2 temperature variations is explored. It is shown by using normalized reference curves that the distributions in dynamic activation energies can be measured from the M2 temperature behavior. An example of the M2 analysis for a series of thermosetting polymers with systematically varied dynamic heterogeneity is presented and discussed.
| S-EPMC7432559 | biostudies-literature
Project description:In this article the NMR data from chemical shifts, coupling constants, and structures of all the characterized compounds were provided, beyond a complementary PCA evaluation for the corresponding manuscript (E.G. Alves Filho, L.M.A. Silva, E.M. Teofilo, F.H. Larsen, E.S. de Brito, 2017) [3]. In addition, a complementary assessment from solid-state NMR data was provided. For further chemometric analysis, numerical matrices from the raw 1H NMR data were made available in Microsoft Excel workbook format (.xls).
| S-EPMC5310203 | biostudies-other
Project description:Recently, 1H NMR (nuclear magnetic resonance) spectroscopy was presented as a viable option for the quality assurance of foods and beverages, such as wine products. Here, a complex chemometric analysis of red and white wine samples was carried out based on their 1H NMR spectra. Extreme gradient boosting (XGBoost) machine learning algorithm was applied for the wine variety classification with an iterative double cross-validation loop, developed during the present work. In the case of red wines, Cabernet Franc, Merlot and Blue Frankish samples were successfully classified. Three very common white wine varieties were selected and classified: Chardonnay, Sauvignon Blanc and Riesling. The models were robust and were validated against overfitting with iterative randomization tests. Moreover, four novel partial least-squares (PLS) regression models were constructed to predict the major quantitative parameters of the wines: density, total alcohol, total sugar and total SO2 concentrations. All the models performed successfully, with R2 values above 0.80 in almost every case, providing additional information about the wine samples for the quality control of the products. 1H NMR spectra combined with chemometric modeling can be a good and reliable candidate for the replacement of the time-consuming traditional standards, not just in wine analysis, but also in other aspects of food science.
| S-EPMC7824661 | biostudies-literature
Project description:The hypothesis of the present study is that metabolic phenotyping of blood plasma allows to (i) discriminate between colorectal cancer patients and control subjects and (ii) identify new biomarkers for colorectal cancer. In order to test this hypothesis, the investigators will apply proton nuclear magnetic resonance (1H-NMR) spectroscopy to perform metabolic phenotyping of blood plasma in 50 colorectal cancer patients and 50 control subjects. Multivariate statistics will be performed to assess the discriminative power of the applied methodology in distinguishing between both groups and to identify metabolites with potential as biomarkers for colorectal cancer.
| 2183890 | ecrin-mdr-crc
Project description:Metabolic profiling of urine presents challenges because of the extensive random variation of metabolite concentrations and the dilution resulting from changes in the overall urine volume. Thus statistical analysis methods play a particularly important role; however, appropriate choices of these methods are not straightforward. Here we investigate constant and variance-stabilization normalization of raw and peak picked spectra, for use with exploratory analysis (principal component analysis) and confirmatory analysis (ordinary and Empirical Bayes t-test) in (1)H NMR-based metabolic profiling of urine. We compare the performance of these methods using urine samples spiked with known metabolites according to a Latin square design. We find that analysis of peak picked and logarithm-transformed spectra is preferred, and that signal processing and statistical analysis steps are interdependent. While variance-stabilizing transformation is preferred in conjunction with principal component analysis, constant normalization is more appropriate for use with a t-test. Empirical Bayes t-test provides more reliable conclusions when the number of samples in each group is relatively small. Performance of these methods is illustrated using a clinical metabolomics experiment on patients with type 1 diabetes to evaluate the effect of insulin deprivation.
| S-EPMC2789356 | biostudies-literature
Project description:2D NMR (1)H-X (X=(15)N or (13)C) HSQC spectra contain cross-peaks for all XHn moieties. Multiplicity-edited(1)H-(13)C HSQC pulse sequences generate opposite signs between peaks of CH(2) and CH/CH(3) at a cost of lower signal-to-noise due to the (13)C T(2) relaxation during an additional 1/(1)JCH period. Such CHn-editing experiments are useful in assignment of chemical shifts and have been successfully applied to small molecules and small proteins (e.g. ubiquitin) dissolved in deuterated solvents where, generally, peak overlap is minimal. By contrast, for larger biomolecules, peak overlap in 2D HSQC spectra is unavoidable and peaks with opposite phases cancel each other out in the edited spectra. However, there is an increasing need for using NMR to profile biomolecules at natural abundance dissolved in water (e.g., protein therapeutics) where NMR experiments beyond 2D are impractical. Therefore, the existing 2D multiplicity-edited HSQC methods must be improved to acquire data on nuclei other than (13)C (i.e.(15)N), to resolve more peaks, to reduce T(2) losses and to accommodate water suppression approaches. To meet these needs, a multiplicity-separated(1)H-X HSQC (MS-HSQC) experiment was developed and tested on 500 and 700 MHz NMR spectrometers equipped with room temperature probes using RNase A (14 kDa) and retroviral capsid (26 kDa) proteins dissolved in 95% H(2)O/5% D(2)O. In this pulse sequence, the 1/(1)JXH editing-period is incorporated in to the semi-constant time (semi-CT) X resonance chemical shift evolution period, which increases sensitivity, and importantly, the sum and the difference of the interleaved (1)J(XH)-active and the (1)J(XH)-inactive HSQC experiments yield two separate spectra for XH(2) and XH/XH(3). Furthermore we demonstrate improved water suppression using triple xyz-gradients instead of the more widely used z-gradient only water-suppression approach.
| S-EPMC6057795 | biostudies-literature
Project description:BackgroundOne-dimensional 1H-NMR spectroscopy is widely used for high-throughput characterization of metabolites in complex biological mixtures. However, the accurate identification of individual compounds is still a challenging task, particularly in spectral regions with higher peak densities. The need for automatic tools to facilitate and further improve the accuracy of such tasks, while using increasingly larger reference spectral libraries becomes a priority of current metabolomics research.ResultsWe introduce a web server application, called MetaboHunter, which can be used for automatic assignment of 1H-NMR spectra of metabolites. MetaboHunter provides methods for automatic metabolite identification based on spectra or peak lists with three different search methods and with possibility for peak drift in a user defined spectral range. The assignment is performed using as reference libraries manually curated data from two major publicly available databases of NMR metabolite standard measurements (HMDB and MMCD). Tests using a variety of synthetic and experimental spectra of single and multi metabolite mixtures show that MetaboHunter is able to identify, in average, more than 80% of detectable metabolites from spectra of synthetic mixtures and more than 50% from spectra corresponding to experimental mixtures. This work also suggests that better scoring functions improve by more than 30% the performance of MetaboHunter's metabolite identification methods.ConclusionsMetaboHunter is a freely accessible, easy to use and user friendly 1H-NMR-based web server application that provides efficient data input and pre-processing, flexible parameter settings, fast and automatic metabolite fingerprinting and results visualization via intuitive plotting and compound peak hit maps. Compared to other published and freely accessible metabolomics tools, MetaboHunter implements three efficient methods to search for metabolites in manually curated data from two reference libraries.
| S-EPMC3213069 | biostudies-literature
Project description:The characteristic signals observed in NMR spectra encode essential information on the structure of small molecules. However, extracting all of this information from complex signal patterns is not trivial. This report demonstrates how computer-aided spectral analysis enables the complete interpretation of 1D (1)H NMR data. The effectiveness of this approach is illustrated with a set of organic molecules, for which replicas of their (1)H NMR spectra were generated. The potential impact of this methodology on organic chemistry research is discussed.
| S-EPMC3812940 | biostudies-literature
Project description:Nuclear magnetic resonance based measurements of small molecule mixtures continues to be confronted with the challenge of spectral assignment. While multi-dimensional experiments are capable of addressing this challenge, the imposed time constraint becomes prohibitive, particularly with the large sample sets commonly encountered in metabolomic studies. Thus, one-dimensional spectral assignment is routinely performed, guided by two-dimensional experiments on a selected sample subset; however, a publicly available graphical interface for aiding in this process is currently unavailable. We have collected spectral information for 360 unique compounds from publicly available databases including chemical shift lists and authentic full resolution spectra, supplemented with spectral information for 25 compounds collected in-house at a proton NMR frequency of 900 MHz. This library serves as the basis for MetaboID, a Matlab-based user interface designed to aid in the one-dimensional spectral assignment process. The tools of MetaboID were built to guide resonance assignment in order of increasing confidence, starting from cursory compound searches based on chemical shift positions to analysis of authentic spike experiments. Together, these tools streamline the often repetitive task of spectral assignment. The overarching goal of the integrated toolbox of MetaboID is to centralize the one dimensional spectral assignment process, from providing access to large chemical shift libraries to providing a straightforward, intuitive means of spectral comparison. Such a toolbox is expected to be attractive to both experienced and new metabolomic researchers as well as general complex mixture analysts.
| S-EPMC3529807 | biostudies-literature