Acquiring and Analyzing Data Independent Acquisition Proteomics Experiments without Spectrum Libraries
Ontology highlight
ABSTRACT:
INSTRUMENT(S): Q Exactive
ORGANISM(S): Homo Sapiens (ncbitaxon:9606)
SUBMITTER: 
Brian Searle
PROVIDER: MSV000084531 | MassIVE | Sun Nov 03 14:04:00 GMT 2019
REPOSITORIES: MassIVE
Similar Datasets
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010708 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010705 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010690 | Pride
Project description:Cerebrospinal fluid (CSF) is in direct contact with the brain and serves as a valuable specimen to examine diseases of the central nervous system through analyzing its components. These include the analysis of metabolites, cells as well as proteins. For identifying new suitable diagnostic protein biomarkers bottom-up data-dependent acquisition (DDA) mass spectrometry-based approaches are most popular. Drawbacks of this method are stochastic and irreproducible precursor ion selection. Recently, data-independent acquisition (DIA) emerged as an alternative method. It overcomes several limitations of DDA, since it combines the benefits of DDA and targeted methods like selected reaction monitoring (SRM). We established a DIA method for in-depth proteome analysis of CSF. For this, four spectral libraries were generated with samples from native CSF (n=5) CSF fractionation (15 in total) and substantia nigra fractionation (54 in total) applying to CSF DIA of three replicates. The DDA and DIA methods for CSF were conducted with the same nanoLC parameters using a 180 minute gradient. Compared to a conventional DDA method, our DIA approach both increased the number of identified protein groups with 1574 compared to DDA with 648 over 50 % with a comprehensive spectral library (generated with DDA measurements from five native CSF and 54 substantia nigra fractions) and decreased the coefficient of variation to 6 %, compared to 11 % with a DDA method. We also could show that a sample specific spectral library generated only from native CSF increased the identification reproducibility from three DIA replicates to 90 % (77 % with a DDA method). Moreover, by utilizing a substantia nigra specific spectral library for CSF DIA over 60 brain-originated proteins could be identified compared to only eleven with DDA. In conclusion, the here presented optimized DIA method substantially outperforms DDA and could develop into a powerful tool for biomarker discovery in CSF.
2018-09-17 | PXD010698 | Pride
Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.
2019-11-08 | PXD012987 | Pride
Project description:For the complete description see the manuscript. Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined.
2019-11-08 | PXD012988 | Pride
Project description:Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined. We found, that while libraries of prefractionationed samples are generally larger, the actually yielded identifications are not increased compared to repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantifications is also highly dependent on the applied spectra library and also whether the peptide or protein level is analysed. Overall, the reproducibility and accuracy of DIA is superior to DDA in all analysed approaches.
2019-11-08 | PXD012986 | Pride
Project description:Data dependent acquisition (DDA) is the method of choice for mass spectrometry based proteomics discovery experiments, data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirement to perform a DIA analysis is the availability of spectral libraries for the peptide identification and quantification. Several researches were already conducted regarding the creation of spectral libraries from DDA analyses and obtaining identifications with these in DIA measurements. But so far only few experiments were conducted, to estimate the effect of these libraries on the quantitative level. In this work we created a spike-in gold standard dataset with known contents and ratios of proteins in a complex sample matrix. With this dataset, we first created spectral libraries using different sample preparation approaches with and without sample prefractionation on peptide and protein level. Two different search engines were used for protein identification. In total, five different spike-in states were compared with DIA analyses, comparing eight different spectral libraries generated by varying approaches and one library free method, as well as one default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding analyses was inspected, but also the number of expected and identified significant quantifications and their ratios were thoroughly examined. We found, that while libraries of prefractionationed samples are generally larger, the actually yielded identifications are not increased compared to repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantifications is also highly dependent on the applied spectra library and also whether the peptide or protein level is analysed. Overall, the reproducibility and accuracy of DIA is superior to DDA in all analysed approaches.
2019-11-08 | PXD014956 | Pride
Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
2022-08-12 | PXD034640 | Pride
Project description:Data independent acquisition (DIA or DIA/SWATH ) mass spectrometry has emerged as a primary measurement strategy in the field of quantitative proteomics. diaPASEF is a recent adaptation that leverages trapped ion mobility spectrometry (TIMS) to improve selectivity and increase sensitivity. The complex fragmentation spectra generated by co-isolation of peptides in DIA mode are most typically analyzed with reference to prior knowledge in the form of spectral libraries. The best established method for generating libraries uses data dependent acquisition (DDA) mode, or DIA mode if appropriately deconvoluted, often including offline fractionation to increase depth of coverage,to create spectral libraries. More recently strategies for spectral library generation based on gas phase fractionation (GPF), where a representative sample is injected serially using narrow window DIA methods designed to cover different slices of the precursor space, have been introduced and performed comparably to deep offline fractionation-based libraries for DIA data analysis. Here, we investigated whether an analogous GPF-based library building approach that accounts for the ion mobility (IM) dimension is useful for the analysis of diaPASEF data and can remove the need for offline fractionation. To enable a rapid library development approach for diaPASEF we designed a GPF acquisition scheme covering the majority of multiply charged precursors in the m/z vs 1/K0 space requiring 7 injections of a representative sample and compared this with libraries generated by direct deconvolution-based analysis of diaPASEF data or by deep offline fractionation and ddaPASEF. . We found that the GPF based library outperformed library generation by direct deconvolution of the diaPASEF data, and performed comparably to deep offline fractionation libraries, when analysing diaPASEF data acquired from 200ng of commercial HeLa digest. With the ion mobility integrated GPF scheme we establish a pragmatic approach to rapid and comprehensive library generation for the analysis of diaPASEF data.
2023-08-18 | PXD039343 | Pride