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SCIEX tripletof 6600

<t>TripleTOF</t> 6600 Tandem MS Data of the Phosphopeptide acVWLVDpSK of ZmC 4 -NADP-ME. The detected b (N-terminal, in red) and y (C-terminal, in blue) fragment ions are labeled in the spectrum. Ac denotes N terminus acetylation and pS denotes phosphorylated Ser. Precursor charge: +2; monoisotopic m/z: 484.7265 D (−1.60 milli-mass unit/−3.30 ppm). Confidence (ProteinPilot): 96.4% (confidence threshold for FDR ≤ 1% = 93.7%).

Tripletof 6600, supplied by SCIEX, used in various techniques. Bioz Stars score: 99/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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tripletof 6600 - by Bioz Stars, 2022-05

99/100 stars

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1) Product Images from "Posttranslational Modification of the NADP-Malic Enzyme Involved in C4 Photosynthesis Modulates the Enzymatic Activity during the Day"

Article Title: Posttranslational Modification of the NADP-Malic Enzyme Involved in C4 Photosynthesis Modulates the Enzymatic Activity during the Day

Journal: The Plant Cell

doi: 10.1105/tpc.19.00406

TripleTOF 6600 Tandem MS Data of the Phosphopeptide acVWLVDpSK of ZmC 4 -NADP-ME. The detected b (N-terminal, in red) and y (C-terminal, in blue) fragment ions are labeled in the spectrum. Ac denotes N terminus acetylation and pS denotes phosphorylated Ser. Precursor charge: +2; monoisotopic m/z: 484.7265 D (−1.60 milli-mass unit/−3.30 ppm). Confidence (ProteinPilot): 96.4% (confidence threshold for FDR ≤ 1% = 93.7%).

Figure Legend Snippet: TripleTOF 6600 Tandem MS Data of the Phosphopeptide acVWLVDpSK of ZmC 4 -NADP-ME. The detected b (N-terminal, in red) and y (C-terminal, in blue) fragment ions are labeled in the spectrum. Ac denotes N terminus acetylation and pS denotes phosphorylated Ser. Precursor charge: +2; monoisotopic m/z: 484.7265 D (−1.60 milli-mass unit/−3.30 ppm). Confidence (ProteinPilot): 96.4% (confidence threshold for FDR ≤ 1% = 93.7%).

Techniques Used: Labeling

2) Product Images from "DIA-NN: Neural networks and interference correction enable deep coverage in high-throughput proteomics"

Article Title: DIA-NN: Neural networks and interference correction enable deep coverage in high-throughput proteomics

Journal: bioRxiv

doi: 10.1101/282699

Performance of DIA-NN in the LFQbench test (Complete Figure, of which an extract is shown in Figure 1C ). LFQbench performance of DIA-NN in comparison to Spectronaut. In the LFQbench test, two peptide preparations (yeast and E.coli ) are mixed in two different proportions (A and B), pooled with a human peptide preparation and analysed in triplicates on TripleTOF 6600 1 . The data were processed at 1% precursor q-value; peptide (panel A) and protein (panel B) ratios between the mixtures were visualised using the LFQbench R package (with the dotted lines indicating the expected ratios). DIA-NN demonstrates significantly better quantification precision for both yeast and E.coli peptides and proteins, as evidenced by the box plots for the ratios. DIA-NN also produced better median CV values for human peptides and proteins: 5.4% and 2.9%, respectively, compared to 7.0% and 3.8% for Spectronaut, as calculated by the LFQbench R package.

Figure Legend Snippet: Performance of DIA-NN in the LFQbench test (Complete Figure, of which an extract is shown in Figure 1C ). LFQbench performance of DIA-NN in comparison to Spectronaut. In the LFQbench test, two peptide preparations (yeast and E.coli ) are mixed in two different proportions (A and B), pooled with a human peptide preparation and analysed in triplicates on TripleTOF 6600 1 . The data were processed at 1% precursor q-value; peptide (panel A) and protein (panel B) ratios between the mixtures were visualised using the LFQbench R package (with the dotted lines indicating the expected ratios). DIA-NN demonstrates significantly better quantification precision for both yeast and E.coli peptides and proteins, as evidenced by the box plots for the ratios. DIA-NN also produced better median CV values for human peptides and proteins: 5.4% and 2.9%, respectively, compared to 7.0% and 3.8% for Spectronaut, as calculated by the LFQbench R package.

Techniques Used: Produced

3) Product Images from "A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification"

Article Title: A Prostate Cancer Proteomics Database for SWATH-MS Based Protein Quantification

Journal: Cancers

doi: 10.3390/cancers13215580

Overview of PCa spectral libraries generated in silico and built in-house. Frequency distribution of peptides per protein and ion per peptides of ( A ) in silico library (Disease); ( B ) in-house library built on Orbitrap Fusion Lumos Tribrid Thermo Scientific (Waltham, MA, USA) mass spectrometer (in micro-flow mode); ( C ) in-house library built on Sciex TripleTOF 6600 mass spectrometer (in micro-flow mode, S-Micro); ( D ) in-house library built on Sciex TripleTOF 6600 mass spectrometer (in nano-flow mode, S-Nano); ( E ) retention time (RT) correlation between the four libraries by using S-micro as a base library; ( F ) Venn diagram presenting the proteotypic proteins overlap between the four libraries.

Figure Legend Snippet: Overview of PCa spectral libraries generated in silico and built in-house. Frequency distribution of peptides per protein and ion per peptides of ( A ) in silico library (Disease); ( B ) in-house library built on Orbitrap Fusion Lumos Tribrid Thermo Scientific (Waltham, MA, USA) mass spectrometer (in micro-flow mode); ( C ) in-house library built on Sciex TripleTOF 6600 mass spectrometer (in micro-flow mode, S-Micro); ( D ) in-house library built on Sciex TripleTOF 6600 mass spectrometer (in nano-flow mode, S-Nano); ( E ) retention time (RT) correlation between the four libraries by using S-micro as a base library; ( F ) Venn diagram presenting the proteotypic proteins overlap between the four libraries.

Techniques Used: Generated, In Silico, Mass Spectrometry

4) Product Images from "A Recombinant Protein Biomarker DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins"

Article Title: A Recombinant Protein Biomarker DDA Library Increases DIA Coverage of Low Abundance Plasma Proteins

Journal: bioRxiv

doi: 10.1101/2020.11.11.377309

$Experimental workflow. (a) Construction of recombinant protein spectral library . A total of 36 cancer-associated biomarkers were selected from the literature and our own studies ( Table 1 ) and sorted into 4 groups (A - D) based on their molecular weight (M.W.). Each group of 9 proteins was spiked with vitronectin for retention time (RT) alignment. Proteins were reduced, alkylated and digested with trypsin. DDA was used for protein identification using a SCIEX TripleTOF 6600. Datasets were concatenated to generate a recombinant protein spectral library. (b) Construction of human plasma protein spectral library. CRC plasma (80 from stage I-IV CRCs) and 20 healthy plasma samples were pooled and depleted the top 14 high abundance proteins with an Agilent MARS-14 depletion column. The depleted samples were digested with trypsin followed by peptide fractionation using high pH reverse-phased HPLC. A DDA method was employed as in (a) to construct the plasma protein DDA spectral library. (c) SWATH/DIA protein identification using rPSL or merged libraries. The constructed rPSL or merged libraries (rPSL + plasma protein spectral library) were combined with SWATH/DIA MS analysis to determine whether it was possible to detect tryptic peptide spectra of the 36 cancer-associated proteins in non-depleted human plasma samples obtained from CRC patients (n=5). Following sample preparation (reduction, alkylation and tryptic digestion), SWATH/DIA MS analysis was performed for peptide/protein identification. PeakView and Skyline were employed for MS data extraction and peak selection with 1% FDR filtering. Identified proteins were further filtered using high stringency protein identification criteria (HPP guideline v3.0).$
Figure Legend Snippet: Experimental workflow. (a) Construction of recombinant protein spectral library . A total of 36 cancer-associated biomarkers were selected from the literature and our own studies ( Table 1 ) and sorted into 4 groups (A - D) based on their molecular weight (M.W.). Each group of 9 proteins was spiked with vitronectin for retention time (RT) alignment. Proteins were reduced, alkylated and digested with trypsin. DDA was used for protein identification using a SCIEX TripleTOF 6600. Datasets were concatenated to generate a recombinant protein spectral library. (b) Construction of human plasma protein spectral library. CRC plasma (80 from stage I-IV CRCs) and 20 healthy plasma samples were pooled and depleted the top 14 high abundance proteins with an Agilent MARS-14 depletion column. The depleted samples were digested with trypsin followed by peptide fractionation using high pH reverse-phased HPLC. A DDA method was employed as in (a) to construct the plasma protein DDA spectral library. (c) SWATH/DIA protein identification using rPSL or merged libraries. The constructed rPSL or merged libraries (rPSL + plasma protein spectral library) were combined with SWATH/DIA MS analysis to determine whether it was possible to detect tryptic peptide spectra of the 36 cancer-associated proteins in non-depleted human plasma samples obtained from CRC patients (n=5). Following sample preparation (reduction, alkylation and tryptic digestion), SWATH/DIA MS analysis was performed for peptide/protein identification. PeakView and Skyline were employed for MS data extraction and peak selection with 1% FDR filtering. Identified proteins were further filtered using high stringency protein identification criteria (HPP guideline v3.0).

Techniques Used: Recombinant, Molecular Weight, Peptide Fractionation, High Performance Liquid Chromatography, Construct, Sample Prep, Selection

5) Product Images from "A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS"

Article Title: A comprehensive spectral assay library to quantify the Escherichia coli proteome by DIA/SWATH-MS

Journal: Scientific Data

doi: 10.1038/s41597-020-00724-7

$Data acquisition workflow to generate a comprehensive E. coli assay library, quality evaluation with DIALib-QC and DIA/SWATH-MS quantification by Spectronaut. A comprehensive DIA/SWATH assay library for E. coli was generated from whole cell lysate, fractionated samples, overexpressed proteins, and supplemented with synthetic peptides. Samples were analyzed with data-dependent acquisition (DDA) mass spectrometry on TripleTOF 5600+ and TripleTOF 6600 instruments resulting in 209 data files. To generate a DIA/SWATH library, the raw data files were converted to mzML format using the ABSCIEX converter with the profile mode extraction parameter. The mzML files were searched against the reference proteome using both Comet and X!Tandem search engines. The identified sequences were then statistically validated using the Trans-Proteomic Pipeline (TPP) including PeptideProphet and iProphet. MAYU was applied to control the FDR at the protein level. Using SpectraST, confidently assigned spectra were converted into a redundant spectral library and retention times are normalized in iRT space using RTCatalog, then a consensus spectrum library was generated. The assay library was extracted from the consensus library using the spectrast2tsv.py script. Libraries were evaluated with the DIA Library Quality Control (DIALib-QC, www.swathatlas.org ) tool and their assessment reports were generated. The performance of the TripleTOF E. coli spectral library was evaluated based on the identification and quantitation of peptides and proteins in data-independent acquisition (DIA) methods with different gradient lengths using the Spectronaut analysis software.$
Figure Legend Snippet: Data acquisition workflow to generate a comprehensive E. coli assay library, quality evaluation with DIALib-QC and DIA/SWATH-MS quantification by Spectronaut. A comprehensive DIA/SWATH assay library for E. coli was generated from whole cell lysate, fractionated samples, overexpressed proteins, and supplemented with synthetic peptides. Samples were analyzed with data-dependent acquisition (DDA) mass spectrometry on TripleTOF 5600+ and TripleTOF 6600 instruments resulting in 209 data files. To generate a DIA/SWATH library, the raw data files were converted to mzML format using the ABSCIEX converter with the profile mode extraction parameter. The mzML files were searched against the reference proteome using both Comet and X!Tandem search engines. The identified sequences were then statistically validated using the Trans-Proteomic Pipeline (TPP) including PeptideProphet and iProphet. MAYU was applied to control the FDR at the protein level. Using SpectraST, confidently assigned spectra were converted into a redundant spectral library and retention times are normalized in iRT space using RTCatalog, then a consensus spectrum library was generated. The assay library was extracted from the consensus library using the spectrast2tsv.py script. Libraries were evaluated with the DIA Library Quality Control (DIALib-QC, www.swathatlas.org ) tool and their assessment reports were generated. The performance of the TripleTOF E. coli spectral library was evaluated based on the identification and quantitation of peptides and proteins in data-independent acquisition (DIA) methods with different gradient lengths using the Spectronaut analysis software.

Techniques Used: Generated, Mass Spectrometry, Quantitation Assay, Software

6) Product Images from "A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics"

Article Title: A comprehensive LFQ benchmark dataset on modern day acquisition strategies in proteomics

Journal: Scientific Data

doi: 10.1038/s41597-022-01216-6

Levey-Jennings plot of the standard deviation in peak area for 50 selected precursors acquired in DDA with the TripleTOF 6600+. The upper chart shows two distinct outliers, acquired respectively on the 2 nd and 12 th of December (red boxes). Manual inspection of the data shows these were caused by ( a ) a wrong vial in the sample tray and ( b ) an empty vial. When these two samples are excluded from the Levey-Jennings plot (lower chart), a significant drop in standard deviation over the time period of data acquisition is seen.

Figure Legend Snippet: Levey-Jennings plot of the standard deviation in peak area for 50 selected precursors acquired in DDA with the TripleTOF 6600+. The upper chart shows two distinct outliers, acquired respectively on the 2 nd and 12 th of December (red boxes). Manual inspection of the data shows these were caused by ( a ) a wrong vial in the sample tray and ( b ) an empty vial. When these two samples are excluded from the Levey-Jennings plot (lower chart), a significant drop in standard deviation over the time period of data acquisition is seen.

Techniques Used: Standard Deviation

Schematic overview of the different acquisition strategies/instruments applied in the study. A comprehensive LC-MS/MS dataset was generated using samples composed of commercial Human K562, Yeast and Escherichia coli (E.coli) full proteome digests. Two hybrid proteome samples A and B containing known quantities of Human , Yeast and E.coli tryptic peptides, as described by Navarro et al . were prepared in three consecutive times to include handling variability. Additionally, a QC sample was created by mixing one sixth of each of the six master batches (65% w/w Human , 22.5% w/w Yeast and 12.5% w/w E.coli ). These commercial lysates were measured individually and as triple hybrid proteome mixtures each in triplicate using DDA and DIA acquisition methodologies available on six LC-MS/MS platforms, i.e. SCIEX TripleTOF 5600 and TripleTOF 6600+, Thermo Orbitrap QE HF-X, Waters Synapt G2-Si and Synapt XS and Bruker timsTOF Pro. The complete dataset was made publicly available to the proteomics community through ProteomeXchange with dataset identifier: PXD028735. In addition, a system suitability workflow (AutoQC) was incorporated on each instrument using commercial E.coli lysate digest which were acquired at multiple timepoints throughout each sample batch. The AutoQC data was automatically imported in Skyline and uploaded to the Panorama AutoQC server using AutoQC loader, enabling system suitability assessment of each LC-MS/MS system used in the dataset.

Figure Legend Snippet: Schematic overview of the different acquisition strategies/instruments applied in the study. A comprehensive LC-MS/MS dataset was generated using samples composed of commercial Human K562, Yeast and Escherichia coli (E.coli) full proteome digests. Two hybrid proteome samples A and B containing known quantities of Human , Yeast and E.coli tryptic peptides, as described by Navarro et al . were prepared in three consecutive times to include handling variability. Additionally, a QC sample was created by mixing one sixth of each of the six master batches (65% w/w Human , 22.5% w/w Yeast and 12.5% w/w E.coli ). These commercial lysates were measured individually and as triple hybrid proteome mixtures each in triplicate using DDA and DIA acquisition methodologies available on six LC-MS/MS platforms, i.e. SCIEX TripleTOF 5600 and TripleTOF 6600+, Thermo Orbitrap QE HF-X, Waters Synapt G2-Si and Synapt XS and Bruker timsTOF Pro. The complete dataset was made publicly available to the proteomics community through ProteomeXchange with dataset identifier: PXD028735. In addition, a system suitability workflow (AutoQC) was incorporated on each instrument using commercial E.coli lysate digest which were acquired at multiple timepoints throughout each sample batch. The AutoQC data was automatically imported in Skyline and uploaded to the Panorama AutoQC server using AutoQC loader, enabling system suitability assessment of each LC-MS/MS system used in the dataset.

Techniques Used: Liquid Chromatography with Mass Spectroscopy, Generated