Xcalibur qual browser 2

Manufactured by Thermo Fisher Scientific

Sourced in Germany

The Xcalibur Qual Browser 2.2 is a software tool designed for data processing and visualization of mass spectrometry data. It provides a user-friendly interface for viewing and analyzing qualitative data generated by various mass spectrometry instruments.

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Lab products found in correlation

Compound discoverer 3, by Thermo Fisher Scientific (3 mentions) Easy nlc 1000 liquid chromatograph, by Thermo Fisher Scientific (1 mentions) Ltq orbitrap velos pro system, by Thermo Fisher Scientific (1 mentions) U3000 rslcnano, by Thermo Fisher Scientific (1 mentions) Sieve 2, by Thermo Fisher Scientific (1 mentions) Prism 6, by GraphPad (1 mentions)

13 protocols using xcalibur qual browser 2

Glycan Structural Characterization

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Theoretical structures and m/z values of derivatized glycans were generated by GlycoWorkbench Software developed by the EUROCarbDB. The experimental value was used to confirm the results. The identified glycans were quantified manually with Xcalibur Qual Browser 2.1 (Thermo Fisher Scientific) using the peak area from the extracted ion chromatogram (XIC) with the following settings: (i) peaks were extracted with a 1 Da (±0.5 Da) mass window, (ii) scan filter was set as full MS, and (iii) genesis peak detection algorithm was used. Nomenclature of glycan is used according to Essentials of Glycobiology [21 ] and the abbreviations are used according to NIBRT GlycoBase.

Yin H., Zhu J., Wu J., Tan Z., An M., Zhou S., Mechref Y, & Lubman D.M. (2016). A procedure for the analysis of site-specific and structure-specific fucosylation in alpha-1-antitrypsin. Electrophoresis, 37(20), 2624-2632.

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Peptide Analysis by Mass Spectrometry

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Peptide samples were dissolved in 0.4% acetic acid and analyzed as previously described with an LTQ-Velos mass instrument (Thermo Fisher Scientific Inc.) equipped with EASY-nLC 1000 Liquid Chromatograph (Yoon et al., 2013 (link)). The acquired mass spectral data were analyzed by SEQUEST search algorithm (Eng et al., 1995 (link)) with the options of average mass (m/z); precursor mass tolerance, 0.8 Da; fragment mass tolerance, 0.8 Da; variable modifications for cysteine, including NEM (m/z difference, 125.125), carbamidomethylation with IAA (57.021), oxidation (15.995) and dioxidation (31.999), and histidine protonation (1.008). Disulfide-bonded peptides were analyzed by in-house Excel program with the option of 2 H loss (−2.016) from all combinations of 2 peptides, each containing cysteine. The extracted ion chromatograms (XIC) were generated by Thermo Xcalibur Qual Browser 2.1 with the precursor ion mass (m/z) and mass tolerance of 0.2 Da.

Yoon B.Y., Yeom J.H., Kim J.S., Um S.H., Jo I., Lee K., Kim Y.H, & Ha N.C. (2014). Direct ROS Scavenging Activity of CueP from Salmonella enterica serovar Typhimurium. Molecules and Cells, 37(2), 100-108.

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Glycan Structure Identification via MS

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Structures and m/z values of derivatized glycans were calculated and identified by GlycoWorkbench Software. GlycoWorkbench is a software tool developed by the EUROCarbDB initiative to assist the manual interpretation of MS data.^{16 (link)} Often used residues of glycans are included in the software. The list of structural constituents comprises an exhaustive collection of saccharides, substituents, reducing-end markers and saccharide modifications. All the stereo-chemical information about a glycan, including the linkage position and ring configuration, can be specified. The display of a glycan is dependent only on its structure and the chosen notation. In our case, by specifying the molecular weight of labeling reagent, we applied the tool to calculate the theoretical exact mass of each derived glycan first and then input the experimented m/z value to search for structures to confirm the results. The identified glycans were quantified automatically with Xcalibur Qual Browser 2.1 (Thermo Fisher Scientific, San Jose, CA) using the peak area from the extracted ion chromatogram (XIC) with the following settings: (1) peaks were extracted with a 1 Da (±0.5 Da) mass window, (2) scan filter was set as full MS, and (3) Genesis peak detection algorithm was used.

Zhang Y., Zhu J., Yin H., Marrero J., Zhang X.X, & Lubman D.M. (2015). An ESI-LC-MS Method for Haptoglobin Fucosylation Analysis in Hepatocellular Carcinoma and Liver Cirrhosis. Journal of proteome research, 14(12), 5388-5395.

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Glycopeptide Identification and Quantification

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The mass spectrum was searched with
Proteome Discoverer 1.2 (Thermo Fisher Scientific, San Jose, CA) software
with SEQUEST using the following settings: (1) fixed modification:
cysteine carbamidomethylation (+57.0 Da); (2) dynamic modification:
methionine oxidation (+16.0 Da), addition of GlcNAc (+203.1 Da), or
GlcNAc+Fucose (+349.2 Da) to asparagine residue; (3) one missed cleavage
was allowed; (4) peptide ion tolerance: 1.4 Da; (5) fragment ion tolerance:
0.8 Da; and (6) Swiss-Prot Homo sapiens database (release 2010_10, downloaded on Nov 2, 2010) was used.
The identified target peptides were quantified manually with Xcalibur
Qual Browser 2.1 (Thermo Fisher Scientific, San Jose, CA) using the
peak area from the extracted ion chromatogram (XIC) with the following
settings: (1) precursor peaks were extracted with a 1 Da (±0.5
Da) mass window, (2) scan filter was set as full MS, (3) boxcar-type
of smoothing with 7 points was enabled, and (4) Genesis peak detection
algorithm was used.

Yin H., Lin Z., Nie S., Wu J., Tan Z., Zhu J., Dai J., Feng Z., Marrero J, & Lubman D.M. (2014). Mass-Selected Site-Specific Core-Fucosylation of Ceruloplasmin in Alcohol-Related Hepatocellular Carcinoma. Journal of Proteome Research, 13(6), 2887-2896.

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MALDI Imaging Data Preprocessing

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Each line scan was converted to imzML file consisting of 75 pixels (spectra) using a dummy .xml position file. This resulted in three MALDI and three MALDI-2 datasets per step size and matrix type. While no lower limit was set for peak intensity for peak picking during data import, the minimum peak frequency was set to 50% (meaning that peaks had to appear in at least half of the pixels in any given line) with a tolerance for peak alignment of 3 ppm. In this way, only reproducible signals were considered and very low abundance peaks close to the detection limit and/or corresponding to random electronic noise were discarded. All scans were recalibrated using up to three peaks, [cholesterol-H₂O+H]⁺, [PE(38:4)+H]⁺, and [PC(34:2)+K]⁺, during data import. The final ID list was then manually curated and several seemingly spurious identifications were removed. Single-scan noise values were taken from Xcalibur Qual Browser 2.3 (Thermo Fisher Scientific GmbH, Bremen, Germany).

Bowman A.P., Bogie J.F., Hendriks J.J., Haidar M., Belov M., Heeren R.M, & Ellis S.R. (2019). Evaluation of lipid coverage and high spatial resolution MALDI-imaging capabilities of oversampling combined with laser post-ionisation. Analytical and Bioanalytical Chemistry, 412(10), 2277-2289.

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Isotopomer Analysis for Metabolomics

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Data were both manually queried and automatically
processed within Xcalibur Qual Browser 2.3 (Thermo Fisher Scientific).
Unless otherwise indicated, data were extracted with ±5–10
ppm mass error tolerances and peak areas (area-under-the-curve, AUC)
determined via the ICIS peak detection algorithm. Regression and statistical
analyses were performed in Origin (version 8.5.1 SR2, Origin Lab,
Northampton, MA). Isotopomer abundance or ratio errors were calculated
from the theoretical isotopomer abundances expected for a given elemental
formula. For EPA 8270 pesticide analysis, the ratio of first isotopomer
to the sum of the monoisotopomer and first isotopomer abundances was
used. For Arabidopsis thaliana metabolomic analysis,
the percent errors of the abundances of the first, second, and third
isotopomers relative to the monoisotopomer abundance were used, conforming
to the standard used by the Seven Golden Rules Excel macro.^{7 ,13 (link)}

Peterson A.C., Hauschild J.P., Quarmby S.T., Krumwiede D., Lange O., Lemke R.A., Grosse-Coosmann F., Horning S., Donohue T.J., Westphall M.S., Coon J.J, & Griep-Raming J. (2014). Development of a GC/Quadrupole-Orbitrap Mass Spectrometer, Part I: Design and Characterization. Analytical Chemistry, 86(20), 10036-10043.

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Glycopeptide Profiling by ETD-MS

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All spectra were analyzed using Xcalibur Qual Browser 2.2 (Thermo Fisher Scientific) software. First, XIC of each precursor ion was obtained from the full-mass scan. Theoretical lists of IgA1 HR peptides fragmented by ETD were generated by using the ProteinProspector MS product tool (http://prospector.ucsf.edu/), with the inclusion of c and z ions (Supplementary Tables S1–S3)^{38 (link)}. To define the time range within which each product ion was detected, the ion chromatograms were extracted automatically by entering the mass range (theoretical m/z ± 10 ppm) of each product ion to Xcalibur Qual Browser system. The attachment sites of Gd O-glycans were manually assigned based on the combination of c ions and z ions.
After determining the attachment sites, the AUC from the precursor ion XIC at the time at which each Gd O-glycopeptide isomer was detected was measured. The percentage against total AUC of precursor ion XIC was expressed as RA.

Ohyama Y., Yamaguchi H., Nakajima K., Mizuno T., Fukamachi Y., Yokoi Y., Tsuboi N., Inaguma D., Hasegawa M., Renfrow M.B., Novak J., Yuzawa Y, & Takahashi K. (2020). Analysis of O-glycoforms of the IgA1 hinge region by sequential deglycosylation. Scientific Reports, 10, 671.

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Quantitative IgA1 O-Glycopeptide Analysis

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All spectra were analyzed using the Xcalibur Qual Browser 2.2 (Thermo Fisher Scientific) software. Individual IgA1 O-glycopeptides were identified by referencing the theoretical monoisotopic mass list, which was created based on mass values of trypsin- digested IgA1 HR amino acid sequences using the GlycoMod tool (http://www.expasy.org) (Ohyama et al., 2020 (link); Renfrow et al., 2007 (link); Takahashi et al., 2010 (link), 2012 (link), 2014 (link)). The ion chromatogram was extracted from five isotopic peaks of each glycopeptide ion, and the area under the curve (AUC) was obtained. To increase the throughput of the analysis, the in-house automated program Glycan Analyzer (MKI, Tokyo, Japan) was used for spectra identification and AUC acquisition of each glycopeptide. Relative abundance (RA, %) of each glycopeptide was obtained by dividing the AUC of each glycopeptide extracted ion chromatogram (XIC) by the total AUC for all glycopeptide XIC. The amounts of GalNAc and Gal per HR were calculated according to the following equation (Wada et al., 2010 (link)):
Amount of GalNAc (or Gal) = ∑{glycopeptide relative abundance % × 10⁻² × number of GalNAc (or Gal) in the glycopeptide}.

Ohyama Y., Yamaguchi H., Ogata S., Chiurlia S., Cox S.N., Kouri N.M., Stangou M.J., Nakajima K., Hayashi H., Inaguma D., Hasegawa M., Yuzawa Y., Tsuboi N., Renfrow M.B., Novak J., Papagianni A.A., Schena F.P, & Takahashi K. (2022). Racial heterogeneity of IgA1 hinge-region O-glycoforms in patients with IgA nephropathy. iScience, 25(11), 105223.

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Identification of Glucose Glycosides

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All downstream data analysis was performed with Compound Discoverer 3.1 and Xcalibur QualBrowser 2.2 (Thermo Fisher). Briefly, all MS1 ions with a signal to noise of greater than 10:1 from the vendor .RAW files were considered for downstream analysis. The LCMS files were chromatographically aligned using an adaptive curve on all ions within a maximum mass shift of 2 minutes and with less than a 5 ppm mass discrepancy. The files were also normalized to compensate for concentration and loading differences between samples using a constant mean normalization. Ion identities were assigned using the mzCloud and ChemSpider databases using a maximum mass tolerance of 5ppm against library entries. In addition, a similarity search algorithm and custom compound class scoring module were used to flag ions that exhibited common glucose ions following fragmentation. Compounds of interest were flagged in the resulting output report by use of custom filter that eliminated ions that were of decreased abundance in the EstG reacted periplasm relative to both the unreacted periplast fraction and the periplast fraction treated with the EstG_S101A.

Daitch A.K., Orsburn B.C., Chen Z., Alvarez L., Eberhard C.D., Sundararajan K., Zeinert R., Kreitler D.F., Jakoncic J., Chien P., Cava F., Gabelli S.B, & Goley E.D. (2022). EstG is a novel esterase required for cell envelope integrity in Caulobacter. Current biology : CB, 33(2), 228-240.e7.

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Comprehensive Analytical Techniques for Biological Research

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Cell shape analysis was performed using Celltool, as indicated in the Method Details section. Tn-Seq analysis using Je, BWA, Samtools, BEDTools, Integrative Genomics Viewer, and the edgeR package in the Bioconductor suite, as described in the Method Details section. Crystallography data is rendered from PyMOL (v2.2.3, Schrödinger, LLC). Crystallography data used PHASER, REFMAC5, Coot and the PDB deposition tools. LCMS analysis completed using Compound Discoverer 3.1 and Xcalibur QualBrowser 2.2 (Thermo Fisher). Prism GraphPad was used perform statistical analyses. Crystallography data was outlined based on Ramachandran statistics (Table S2). Information regarding individual statistical test parameters can be found in the figure legends.

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