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Mse data viewer

Manufactured by Waters Corporation
Sourced in United States

The MSE data viewer is a software application developed by Waters Corporation to visualize and analyze data generated by their mass spectrometry equipment. It provides users with tools to view and interact with the data in a user-friendly interface.

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2 protocols using mse data viewer

1

Comprehensive Lipidomic Analysis with LipoStar

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Identification and structural characterization of significant lipid features were confirmed with LipoStar(Goracci et al., 2017 (link)) (Version 1.0.4, Molecular Discovery, UK). Lipidomix standard (Avanti Polar Lipids, INC., Alabaster, AL, USA) and quality control samples were analyzed in LipoStar with the recommended data processing parameters(Goracci et al., 2017 (link)) except MS/MS signal filtering threshold was set to 20 for both positive and negative ionization mode. The precursor ion (MS) and fragment ion information obtained by data independent MS (MSE) were automatically annotated using LipoStar database library with mass tolerances of 5 ppm and 10 ppm, respectively. Annotated lipid species with the highest score and high-confidence identification (matches between experimental and theoretical MS/MS spectra) were approved, and the identified lipids with low-confidence matches were further evaluated manually using MSE data viewer (Version 1.3, Waters Corp., MA, USA). In case of co-eluting and isomeric lipid species (e.g., triacylglycerols), all the available fragments in MS/MS spectra data were reported. However, if fragments for fatty acyl compositions of lipids weren’t clear, the lipid species were annotated as sum of total number of carbons and double bonds (e.g. PC 32:2).
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2

Transcriptomic and Lipidomic Analysis of Human Meibum

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The transcriptomic datasets were processed using Expression and Transcriptome Analysis Consoles (v.4.0.1.36; both from Affymetrix) and SigmaStat (v.3.5, from Systat Software, Inc., San Jose, CA, USA). The default (and currently the industry standard) filter criteria: (1) (+2) < LFC < (−2), and (2) ANOVA p-value (condition pair) ≤0.05, were used to analyze the data. A tighter LFC of >(+1.2) and <(−1.2), as proposed in [34 (link)], was also tested, but deemed impractical because of an unrealistically high number of samples needed to satisfy statistical criteria (see Discussion).
The RP-UPLC/MS data were analyzed using MassLynx (v.4.1), MSe Data Viewer (v.1.4), and Progenesis QI software packages (from Waters). A Supplemental Table S1 lists major lipids of human meibum relevant to this study, and their corresponding m/z values. SigmaStat and SigmaPlot software packages from Systat Software, Inc. were used to conduct statistical evaluation of the data.
The transcriptomic and lipidomic data for two genders were compared gender-wide using Student’s t-test for the two groups. Tests with p-values ≤ 0.05 were considered statistically significant. Principal component analyses were performed using Transcriptome Analysis Console, Progenesis QI, and EZInfo (v.3.0.3.0 from Umetrics AB, Umeå, Sweden).
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