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Dataset results
14 results for “LC-MS analysis”
Lipidomics LC-MS analysis support tools for outlier detection
<p>Identification of features with high levels of confidence in liquid chromatography-mass spectrometry (LC MS) lipidomics research is an essential part of biomarker discovery, but existing software platforms can give inconsistent results, even from identical spectral data. This poses a clear challenge for reproducibility in bioinformatics work, and highlights the importance of data-driven outlier detection in assessing spectral outputs – here demonstrated using a machine learning approach based on support vector machine regression combined with leave-one-out cross validation – as well as manual curation, in order to identify software-driven errors driven by closely related lipids and by co-elution issues.</p> <p>The lipidomics case study dataset used in this work analysed a lipid extraction of a human pancreatic adenocarcinoma cell line (PANC-1, Merck, UK, cat no. 87092802) analysed using an Acquity M-Class UPLC system (Waters, UK) coupled to a ZenoToF 7600 mass spectrometer (Sciex, UK). Raw output files are included alongside processed data using MS DIAL (v4.9.221218) and Lipostar (v2.1.4) and a Jupyter notebook with Python code to analyse the outputs for outlier detection.</p>
Data analysis of an LC-MS dataset from a human urine biofluid cohort study
<p>Supplementary dataset and tutorials for the "<strong>Statistical analysis in metabolic phenotyping"</strong></p> <p> </p> <p>This repository contains Jupyter Notebooks with two examplar metabolomic data analysis workflows, applied to a liquid chromatography mass spectrometry dataset (LC-MS). The LC-MS dataset used comes from a metabolic phenotyping investigation of human urine biofluid samples from a dementia cohort. In this sample set, baseline spot urine samples (first sample collected after recruitment to the study) were collected as part of the AddNeuroMed<sup>1</sup> and ART/DCR study consortia, with the aim of identifying biomarkers of neurocognitive decline and Alzheimer’s disease. These samples were analysed by LC-MS and <sup>1</sup>H NMR, using the methods described by Lewis <em>et al</em><sup>2</sup> and Dona <em>et al</em>. Detailed information about this cohort and other available phenotypic measurements can be found in Lovestone and the ANMERGE<sup>3</sup> repository, which can be accessed via the Sage BioNetworks portal (<a href="https://doi.org/10.7303/syn22252881">https://doi.org/10.7303/syn22252881</a>). Information about the metabolic profiling experiments can be found in the study's MetaboLights entry: <a href="https://www.ebi.ac.uk/metabolights/MTBLS719">https://www.ebi.ac.uk/metabolights/MTBLS719</a>.</p> <p> </p> <p>1. Lovestone, S. <em>et al.</em> AddNeuroMed - The european collaboration for the discovery of novel biomarkers for alzheimer’s disease. in <em>Annals of the New York Academy of Sciences</em> (2009). doi:10.1111/j.1749-6632.2009.05064.x</p> <p>2. Lewis, M. R. <em>et al.</em> Development and Application of UPLC-ToF MS for Precision Large Scale Urinary Metabolic Phenotyping. <em>Anal. Chem.</em> <strong>88</strong>, acs.analchem.6b01481 (2016).</p> <p>3. Birkenbihl, C. <em>et al.</em> ANMerge: A comprehensive and accessible Alzheimer’s disease patient-level dataset. <em>medRxiv</em> (2020). doi:10.1101/2020.08.04.20168229</p>
Positive mode DDA LC-MS/MS analysis of organic plant extracts (Malaga)
<p>Organic Plant extracts from Malaga resuspended in 8/2 MeOH/water and analysed on Exploris 480 in positive DDA mode.</p>
Lactobacillus johnsonii N6.2 total lipid and fractionated lipids profiling by qualitative lipidomic LC-MS/MS analysis
<p>Table presents both MS-1 (precursor mass-matched) and MS-2 (precursor mass- and spectral-matched) annotations. The values in the table represent the peak area. The column labels, RT: retention time; m/z: mass-to-charge ratio; SL (simple lipids), GL (glycolipids), and PL (phospholipids) represent lipid fractions and TL: total lipids. Table is provided as an xlsx file.</p>
LC-MS/MS analysis of Buxus
<p>Lc-MS/MS analysis of the polar extracts of <em>Buxus</em></p>
Fig. 5. A in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow
Fig. 5. A boxplot showing the ion intensities of MS/MS feature 10 (gallic acid) in extracts which were active (IC50 <30 μg/mL) and inactive against α-glucosidase.
Fig. 4 in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow
Fig. 4. Discrimination of the analyzed Alnus extracts into chemogroups. The analyzed extracts can be discriminated into three chemogroups by visualizing the CSCS distance metric between samples as PCoA plot (A) and chemical dendrogram (B). On the other hand, conventional methods such as PCA score plot (C) or hierarchical clustering analysis (HCA) using the Euclidean distance (D; chemogroups 1–3 are visualized with same colors used in B to make it easy to be compared) could not discriminate the samples into the same chemotypes. By mapping the chemogrouping of samples on the molecular network, it could be visualized that the three chemogroups were rich in diarylheptanoid, flavonoid, and tannins, respectively (E). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow
Fig. 3. MS2LDA-driven substructural annotation of diarylheptanoids of Alnus species. Integrated with GNPS library matching and NAP in silico annotation, diarylheptanoid-related Mass2Motifs 41, 49, 72, and 81 could be characterized and correlated with specific substructures of diarylheptanoid aglycones. Scaffold diversity within diarylheptanoid molecular families A, D, and I were revealed by mapping these Mass2Motifs on the molecular network with different colors. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 1 in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow
Fig. 1. LC–MS base peak ion (BPI) chromatograms of 15 Alnus extracts. Gaps between chromatogram were added to visualize their difference, so y-axis values do not equal to the absolute intensities.
Fig. 2 in Assessing specialized metabolite diversity of Alnus species by a digitized LC-MS/MS data analysis workflow
Fig. 2. The MS/MS spectral network of specialized metabolites contained in the bark, twigs, leaves, and fruits of A. japonica, A. firma, A. hirsuta, and A. hirsuta var. sibirica. Spectral nodes are colored according to the mean precursor ion intensity per different plant parts: bark, twigs, leaves, and fruits. Molecular families A–I are highlighted.
LC-MS analysis raw data of DTX3L-mediated enzymatic conjugation of ubiquitin with various nucleotide substrates
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Preparation of Deuterium-Labeled Armodafinil by Hydrogen–Deuterium Exchange and Its Application in Quantitative Analysis by LC-MS
<p>Armodafinil, the R enantiomer of modafinil, was approved in 2007 by the US Food and<br> Drug Administration as a wake-promoting agent for excessive sleepiness treatment. Due to its<br> abuse by students and athletes, there is a need of its quantification. Quantitative analysis by liquid<br> chromatography-mass spectrometry, however, though very common and sensitive, frequently cannot<br> be performed without isotopically labeled standards which usually have to be specially synthesized.<br> Here we reported our investigation on the preparation of deuterated standard of armodafinil based<br> on the simple and inexpensive hydrogen–deuterium exchange reaction at the carbon centers. The<br> obtained results clearly indicate the possibility of introduction of three deuterons into the armodafinil<br> molecule. The introduced deuterons do not undergo back exchange under neutral and acidic<br> conditions. Moreover, the deuterated and non-deuterated armodafinil isotopologues revealed coelution<br> during the chromatographic analysis. The ability to control the degree of deuteration using<br> different reaction conditions was determined. The proposed method of deuterated armodafinil<br> standard preparation is rapid, cost-efficient and may be successfully used in its quantitative analysis<br> by LC-MS.</p>
GNPS - Non-targeted 2D LC-MS/MS analysis of NEHLA Dissolved Organic Matter (2/2)
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GNPS - Non-targeted 2D LC-MS/MS analysis of NEHLA Dissolved Organic Matter (1/2)
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.