Structured Review

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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Images

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

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    SCIEX sciex tripletof 6600
    Phosphopeptide (p-pep) profiling of MagReSyn® microparticles by LC-MS/MS on AB <t>Sciex</t> TripleTOF 6600. (A) Number of p-pep identified for each condition (blue bars) and the selectivity of each enrichment (orange dots). (B) Mono-(blue), di-(orange) and multi-phosphorylated (grey) peptide distribution in each condition. (C) Difference in percentage of identified p-pep between the optimized solvent (S1, S2, S3) and the standard (Std).
    Sciex 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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    Average 99 stars, based on 1 article reviews
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    86
    SCIEX tripletof 6600 mass spectrometer
    Zeno SWATH and its performance on K562 using 5 µl/min, 20 min micro-flow chromatography. a) The Zeno SWATH DIA process. a) Q1 quad dimension, a typical TOF-MS showing variable window distribution. b) ZenoTOF 7600 system ion path. c) Expanded view of the Zeno trap. d) MS/MS spectra for selected precursor as highlighted in a). b , c) Reproducibility of protein identification using SWATH and Zeno SWATH on human cell-line standards separated by micro-flow chromatography with 62.5 ng K562 load . Average identification numbers of proteins (b) and precursors (c) across three technical replicates (grey background bar) are given; numbers of consistent identifications in technical replicates are given in dark grey; proteins or precursors quantified with coefficient of variation better than 20% in grey, and those quantified with a CV better than 10% in light grey. d) Protein-level LFQbench results for Zeno SWATH . Quantification precision was benchmarked using yeast lysates that were spiked in two different proportions (A and B, three repeat injections each) into human peptide preparation (K562) (A: 30 ng K562 + 35 ng yeast; B: 30 ng K562 + 17.5 ng yeast). Raw data were processed by library-free mode DIA-NN analysis. Protein ratios between the mixtures were visualised using the LFQbench R package [ 21 ]. Left pane, log-transformed ratios (log2(A/B)) of proteins plotted for each benchmarked software tool over the log-transformed intensity of sample B. Coloured dashed lines represent the expected log2(A/B) values for human (green) and yeast (orange). Black dashed lines represent the local trend along the x axis of experimental log-transformed ratios of each population (human, yeast). Right panel, protein quantification performance shown as box plots (boxes, interquartile range; whiskers, 1–99 percentile; n = 3,414 (human) and n = 1,816 (yeast)). e) Visualisation of precursor identification across gradient length with SWATH and Zeno SWATH of 62.5 ng K562 injection. f) Precursor identification performance using SWATH and Zeno SWATH . Illustrated is the average number of precursor identifications for a K562 dilution series under three acquisition methods with library-free DIA-NN analysis. g) Histogram of the protein abundance distribution represented as copy number in log10 scale of a human proteomic reference dataset [ 24 ]. Zeno SWATH increases protein identification numbers by quantifying more low-abundance proteins as detected in a single-shot analysis of 62.5 ng K562 using SWATH on the <t>TripleTOF</t> 6600 system (blue), SWATH on the ZenoTOF 7600 system (yellow), and Zeno SWATH (red).
    Tripletof 6600 Mass Spectrometer, supplied by SCIEX, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    SCIEX tripletof 6600 three species benchmark data
    MaxLFQ for DIA. a , Stacked interquartile rages of protein ratio distributions in the small-ratio four-species dataset from Bruderer et al. 34 using different versions of MaxLFQ for DIA and compared to the results from this publication. MaxDIA is capable of MS1 and MS2 level as well as hybrid quantification modes. b , Quantification of a three-species benchmark mixture measured on a SCIEX <t>TripleTOF</t> 6600 instrument mixing proteomes from three species in defined ratio 2 with MaxLFQ for DIA. The accompanying DDA library was used. The box plots here and in the subsequent panels are based on the numbers of data points given in the tables below the respective plot (valid LFQ ratios). All box plots indicate the median and the first and third quartiles as box ends. Whiskers are positioned 1.5 box lengths away from the box ends. c , Same as b but analyzed with MaxDIA in discovery mode. d , Quantification of a three-species benchmark mixture measured on a Bruker timsTOF Pro instrument mixing proteomes from three species in defined ratio using a DDA library. e , Same as d but analyzed in discovery mode.
    Tripletof 6600 Three Species Benchmark Data, supplied by SCIEX, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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    Phosphopeptide (p-pep) profiling of MagReSyn® microparticles by LC-MS/MS on AB Sciex TripleTOF 6600. (A) Number of p-pep identified for each condition (blue bars) and the selectivity of each enrichment (orange dots). (B) Mono-(blue), di-(orange) and multi-phosphorylated (grey) peptide distribution in each condition. (C) Difference in percentage of identified p-pep between the optimized solvent (S1, S2, S3) and the standard (Std).

    Journal: bioRxiv

    Article Title: Zirconium(IV)-IMAC for phosphopeptide enrichment in phosphoproteomics

    doi: 10.1101/2020.04.13.038810

    Figure Lengend Snippet: Phosphopeptide (p-pep) profiling of MagReSyn® microparticles by LC-MS/MS on AB Sciex TripleTOF 6600. (A) Number of p-pep identified for each condition (blue bars) and the selectivity of each enrichment (orange dots). (B) Mono-(blue), di-(orange) and multi-phosphorylated (grey) peptide distribution in each condition. (C) Difference in percentage of identified p-pep between the optimized solvent (S1, S2, S3) and the standard (Std).

    Article Snippet: The enriched phosphopeptide eluates were analysed by LC-MS/MS on a Sciex TripleTOF 6600 and the raw files were processed for peptide identification using Proteome Discoverer and Mascot as the search engine ( , left branch).

    Techniques: Liquid Chromatography with Mass Spectroscopy

    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.

    Journal: Scientific Data

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

    doi: 10.1038/s41597-020-00724-7

    Figure Lengend 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.

    Article Snippet: Data dependent acquisition (DDA) mass spectrometry for spectral assay library generation DDA was performed on both a TripleTOF 5600+ (SCIEX) and a TripleTOF 6600 mass spectrometer (SCIEX), both interfaced with a micro-LC interfacePlus HPLC system (Eksigent) configured in either nano-flow or micro-flow mode.

    Techniques: Generated, Mass Spectrometry, Quantitation Assay, Software

    Zeno SWATH and its performance on K562 using 5 µl/min, 20 min micro-flow chromatography. a) The Zeno SWATH DIA process. a) Q1 quad dimension, a typical TOF-MS showing variable window distribution. b) ZenoTOF 7600 system ion path. c) Expanded view of the Zeno trap. d) MS/MS spectra for selected precursor as highlighted in a). b , c) Reproducibility of protein identification using SWATH and Zeno SWATH on human cell-line standards separated by micro-flow chromatography with 62.5 ng K562 load . Average identification numbers of proteins (b) and precursors (c) across three technical replicates (grey background bar) are given; numbers of consistent identifications in technical replicates are given in dark grey; proteins or precursors quantified with coefficient of variation better than 20% in grey, and those quantified with a CV better than 10% in light grey. d) Protein-level LFQbench results for Zeno SWATH . Quantification precision was benchmarked using yeast lysates that were spiked in two different proportions (A and B, three repeat injections each) into human peptide preparation (K562) (A: 30 ng K562 + 35 ng yeast; B: 30 ng K562 + 17.5 ng yeast). Raw data were processed by library-free mode DIA-NN analysis. Protein ratios between the mixtures were visualised using the LFQbench R package [ 21 ]. Left pane, log-transformed ratios (log2(A/B)) of proteins plotted for each benchmarked software tool over the log-transformed intensity of sample B. Coloured dashed lines represent the expected log2(A/B) values for human (green) and yeast (orange). Black dashed lines represent the local trend along the x axis of experimental log-transformed ratios of each population (human, yeast). Right panel, protein quantification performance shown as box plots (boxes, interquartile range; whiskers, 1–99 percentile; n = 3,414 (human) and n = 1,816 (yeast)). e) Visualisation of precursor identification across gradient length with SWATH and Zeno SWATH of 62.5 ng K562 injection. f) Precursor identification performance using SWATH and Zeno SWATH . Illustrated is the average number of precursor identifications for a K562 dilution series under three acquisition methods with library-free DIA-NN analysis. g) Histogram of the protein abundance distribution represented as copy number in log10 scale of a human proteomic reference dataset [ 24 ]. Zeno SWATH increases protein identification numbers by quantifying more low-abundance proteins as detected in a single-shot analysis of 62.5 ng K562 using SWATH on the TripleTOF 6600 system (blue), SWATH on the ZenoTOF 7600 system (yellow), and Zeno SWATH (red).

    Journal: bioRxiv

    Article Title: High-throughput proteomics of nanogram-scale samples with Zeno SWATH DIA

    doi: 10.1101/2022.04.14.488299

    Figure Lengend Snippet: Zeno SWATH and its performance on K562 using 5 µl/min, 20 min micro-flow chromatography. a) The Zeno SWATH DIA process. a) Q1 quad dimension, a typical TOF-MS showing variable window distribution. b) ZenoTOF 7600 system ion path. c) Expanded view of the Zeno trap. d) MS/MS spectra for selected precursor as highlighted in a). b , c) Reproducibility of protein identification using SWATH and Zeno SWATH on human cell-line standards separated by micro-flow chromatography with 62.5 ng K562 load . Average identification numbers of proteins (b) and precursors (c) across three technical replicates (grey background bar) are given; numbers of consistent identifications in technical replicates are given in dark grey; proteins or precursors quantified with coefficient of variation better than 20% in grey, and those quantified with a CV better than 10% in light grey. d) Protein-level LFQbench results for Zeno SWATH . Quantification precision was benchmarked using yeast lysates that were spiked in two different proportions (A and B, three repeat injections each) into human peptide preparation (K562) (A: 30 ng K562 + 35 ng yeast; B: 30 ng K562 + 17.5 ng yeast). Raw data were processed by library-free mode DIA-NN analysis. Protein ratios between the mixtures were visualised using the LFQbench R package [ 21 ]. Left pane, log-transformed ratios (log2(A/B)) of proteins plotted for each benchmarked software tool over the log-transformed intensity of sample B. Coloured dashed lines represent the expected log2(A/B) values for human (green) and yeast (orange). Black dashed lines represent the local trend along the x axis of experimental log-transformed ratios of each population (human, yeast). Right panel, protein quantification performance shown as box plots (boxes, interquartile range; whiskers, 1–99 percentile; n = 3,414 (human) and n = 1,816 (yeast)). e) Visualisation of precursor identification across gradient length with SWATH and Zeno SWATH of 62.5 ng K562 injection. f) Precursor identification performance using SWATH and Zeno SWATH . Illustrated is the average number of precursor identifications for a K562 dilution series under three acquisition methods with library-free DIA-NN analysis. g) Histogram of the protein abundance distribution represented as copy number in log10 scale of a human proteomic reference dataset [ 24 ]. Zeno SWATH increases protein identification numbers by quantifying more low-abundance proteins as detected in a single-shot analysis of 62.5 ng K562 using SWATH on the TripleTOF 6600 system (blue), SWATH on the ZenoTOF 7600 system (yellow), and Zeno SWATH (red).

    Article Snippet: For both methods, ion source gas 1 and 2 were set to 12 and 60 psi, respectively; curtain gas to 25, CAD gas to 7, and source temperature to 150°C; spray voltage was set to 4,500 V. K562 dilution series were acquired on a nanoAcquity UPLC System (Waters) coupled to a SCIEX TripleTOF 6600 mass spectrometer.

    Techniques: Chromatography, Tandem Mass Spectroscopy, Transformation Assay, Software, Injection

    MaxLFQ for DIA. a , Stacked interquartile rages of protein ratio distributions in the small-ratio four-species dataset from Bruderer et al. 34 using different versions of MaxLFQ for DIA and compared to the results from this publication. MaxDIA is capable of MS1 and MS2 level as well as hybrid quantification modes. b , Quantification of a three-species benchmark mixture measured on a SCIEX TripleTOF 6600 instrument mixing proteomes from three species in defined ratio 2 with MaxLFQ for DIA. The accompanying DDA library was used. The box plots here and in the subsequent panels are based on the numbers of data points given in the tables below the respective plot (valid LFQ ratios). All box plots indicate the median and the first and third quartiles as box ends. Whiskers are positioned 1.5 box lengths away from the box ends. c , Same as b but analyzed with MaxDIA in discovery mode. d , Quantification of a three-species benchmark mixture measured on a Bruker timsTOF Pro instrument mixing proteomes from three species in defined ratio using a DDA library. e , Same as d but analyzed in discovery mode.

    Journal: Nature Biotechnology

    Article Title: MaxDIA enables library-based and library-free data-independent acquisition proteomics

    doi: 10.1038/s41587-021-00968-7

    Figure Lengend Snippet: MaxLFQ for DIA. a , Stacked interquartile rages of protein ratio distributions in the small-ratio four-species dataset from Bruderer et al. 34 using different versions of MaxLFQ for DIA and compared to the results from this publication. MaxDIA is capable of MS1 and MS2 level as well as hybrid quantification modes. b , Quantification of a three-species benchmark mixture measured on a SCIEX TripleTOF 6600 instrument mixing proteomes from three species in defined ratio 2 with MaxLFQ for DIA. The accompanying DDA library was used. The box plots here and in the subsequent panels are based on the numbers of data points given in the tables below the respective plot (valid LFQ ratios). All box plots indicate the median and the first and third quartiles as box ends. Whiskers are positioned 1.5 box lengths away from the box ends. c , Same as b but analyzed with MaxDIA in discovery mode. d , Quantification of a three-species benchmark mixture measured on a Bruker timsTOF Pro instrument mixing proteomes from three species in defined ratio using a DDA library. e , Same as d but analyzed in discovery mode.

    Article Snippet: SCIEX TripleTOF 6600 three-species benchmark data were obtained from ProteomeXchange ( PXD002952 ).

    Techniques: