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pmid:31699904

Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition.

Katalin Barkovits · Sandra Pacharra · Kathy Pfeiffer · Simone Steinbach · Martin Eisenacher · Katrin Marcus · Julian Uszkoreit

Molecular & cellular proteomics : MCP · 2020 doi:10.1074/mcp.RA119.001714

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title
Reproducibility, Specificity and Accuracy of Relative Quantification Using Spectral Library-based Data-independent Acquisition.
doi
10.1074/mcp.RA119.001714
pmid
31699904
authors array · 7 items
[0]
Katalin Barkovits
[1]
Sandra Pacharra
[2]
Kathy Pfeiffer
[3]
Simone Steinbach
[4]
Martin Eisenacher
[5]
Katrin Marcus
[6]
Julian Uszkoreit
journal
Molecular & cellular proteomics : MCP
year
2020
abstract
Currently data-dependent acquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but data-independent acquisition (DIA) is steadily becoming more important. One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. Overall, the reproducibility and accuracy of DIA quantification is superior to DDA in all applied approaches.Data has been deposited to the ProteomeXchange repository with identifiers PXD012986, PXD012987, PXD012988 and PXD014956.
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Afterward, an analysis of variance (ANOVA) model was fitted to the transformed data. As a post-hoc test Tukey's honest significance test was conducted, to determine, which spike-in states were significantly differential. Finally, the ANOVA p values were corrected for multiple testing using the Benjamini-Hochberg procedure.
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sections[17] "Statistical Analysis" paragraph 2
quote
Afterward, an analysis of variance (ANOVA) model was fitted to the transformed data. As a post-hoc test Tukey's honest significance test was conducted, to determine, which spike-in states were significantly differential. Finally, the ANOVA p values were corrected for multiple testing using the Benjamini-Hochberg procedure.
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Data has been deposited to the ProteomeXchange repository (https://www.ebi.ac.uk/pride/archive) with identifiers PXD012986, PXD012987, PXD012988, and PXD014956. Reviewer access data is provided in the respective document.
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sections[32] "DATA AVAILABILITY" paragraphs 1-3
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Data has been deposited to the ProteomeXchange repository (https://www.ebi.ac.uk/pride/archive) with identifiers PXD012986, PXD012987, PXD012988, and PXD014956. Reviewer access data is provided in the respective document.
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Composition of the gold standard spike-in sample set consisting of a constant C2C12 cell lysate background and varying amounts of 13 non-mouse spike-in proteins. Shown are the pmol amounts of the spike-in proteins for each of the five sample states
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Overview of analyzed DIA libraries. The names of each library consists of the spike-in types (MM: master mix, i.e. mixture of background and spike-ins in fixed amount, GS: gold standard, i.e. the same samples as measured by DIA), the digest type (IS: in-solution, F: fragmented and therefore in-gel), whether fractionation was performed and on which level (Prot: protein fraction, Pep: peptide fractionation), finally, the number represents the number of MS runs for the library. The libraries generated by Pulsar instead of ProteomeDiscoverer are indicated by the respective prefix
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Overview of analyzed DIA libraries. The names of each library consists of the spike-in types (MM: master mix, i.e. mixture of background and spike-ins in fixed amount, GS: gold standard, i.e. the same samples as measured by DIA), the digest type (IS: in-solution, F: fragmented and therefore in-gel), whether fractionation was performed and on which level (Prot: protein fraction, Pep: peptide fractionation), finally, the number represents the number of MS runs for the library. The libraries generated by Pulsar instead of ProteomeDiscoverer are indicated by the respective prefix
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False positive rate. Number of FP and resulting FP rate after correlation and low-fold change filtering on protein and peptide level
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False positive rate. Number of FP and resulting FP rate after correlation and low-fold change filtering on protein and peptide level
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129
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226
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AUCs and mean absolute percentage errors. Table showing the AUC of the ROCs and the mean absolute percentage error (MAPE) of the respective method on the peptide and protein level. The MAPE was calculated between the median of achieved and expected protein/peptide fold changes, on log2 transformed ratios. The values can be interpreted as deviation between the achieved and expected ratio in percent
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AUCs and mean absolute percentage errors. Table showing the AUC of the ROCs and the mean absolute percentage error (MAPE) of the respective method on the peptide and protein level. The MAPE was calculated between the median of achieved and expected protein/peptide fold changes, on log2 transformed ratios. The values can be interpreted as deviation between the achieved and expected ratio in percent
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Afterward, an analysis of variance (ANOVA) model was fitted to the transformed data. As a post-hoc test Tukey's honest significance test was conducted, to determine, which spike-in states were significantly differential. Finally, the ANOVA p values were corrected for multiple testing using the Benjamini-Hochberg procedure.
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cquisition (DDA) is the method of choice for mass spectrometry-based proteomics discovery experiments, but
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10 min. As spike-in proteins we chose 13 non-mouse proteins with varying physico-chemical prope
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One of the most important requirements to perform a DIA analysis is the availability of suitable spectral libraries for peptide identification and quantification. Several studies were performed addressing the evaluation of spectral library performance for protein identification in DIA measurements. But so far only few experiments estimate the effect of these libraries on the quantitative level.In this work we created a gold standard spike-in sample set with known contents and ratios of proteins in a complex protein matrix that allowed a detailed comparison of DIA quantification data obtained with different spectral library approaches. We used in-house generated sample-specific spectral libraries created using varying sample preparation approaches and repeated DDA measurement. In addition, two different search engines were tested for protein identification from DDA data and subsequent library generation. In total, eight different spectral libraries were generated, and the quantification results compared with a library free method, as well as a default DDA analysis. Not only the number of identifications on peptide and protein level in the spectral libraries and the corresponding DIA analysis results was inspected, but also the number of expected and identified differentially abundant protein groups and their ratios.We found, that while libraries of prefractionated samples were generally larger, there was no significant increase in DIA identifications compared with repetitive non-fractionated measurements. Furthermore, we show that the accuracy of the quantification is strongly dependent on the applied spectral library and whether the quantification is based on peptide or protein level. 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Shown are the pmol amounts of the spike-in proteins for each of the five sample states", "rows": [["", "UniProt accession", "Amount of spike-in proteins (pmol)"], ["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"], ["α-synuclein (pmol)", "P37840", "1", "10", "0.5", "0.1", "5"], ["β-lactoglobulin (pmol)", "P02754", "0.5", "0.1", "5", "10", "1"], ["Fibrinogen (pmol)", "P02671, P02675, P02679", "10", "5", "1", "0.5", "0.1"], ["α, β, γ each"], ["Glucose oxidase (pmol)", "P13006", "0.1", "1", "10", "5", "0.5"], ["Hemoglobin (pmol)", "P69905, P68871", "0.5", "5", "10", "1", "0.1"], ["α, β each"], ["Lipase (pmol)", "P20261, P32946, P32947", "0.1", "0.5", "1", "5", "10"], ["1, 2, 3 each"], ["Lysozyme (pmol)", "P00698", "5", "10", "0.1", "0.5", "1"], ["Myoglobin (pmol)", "P68082", "1", "0.1", "5", "10", "0.5"], ["C2C12 lysate (μg)", "", "20", "20", "20", "20", "20"]], "row_count": 14, "column_count": 7, "numeric_cells": 60, "header_rows": 2, "locator": "section[7]/table-wrap[1]#TI"}}, {"id": "asset:table:pmid:31699904:2", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:94dd730701a6049323856261234d1f083c9f9bb68b7ae1a4b442033948cc9cc9", "bytes": 1113, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[7]/table-wrap[1]#TI", "version": null, "note": "Composition of the gold standard spike-in sample set consisting of a constant C2C12 cell lysate background and varying amounts of 13 non-mouse spike-in proteins. Shown are the pmol amounts of the spike-in proteins for each of the five sample states", "structure": {"pmcid": "PMC6944235", "section_index": 8, "section_title": "Sample Preparation", "row_count": 14, "column_count": 7}, "table": {"label": "Table I", "caption": "Composition of the gold standard spike-in sample set consisting of a constant C2C12 cell lysate background and varying amounts of 13 non-mouse spike-in proteins. Shown are the pmol amounts of the spike-in proteins for each of the five sample states", "rows": [["", "UniProt accession", "Amount of spike-in proteins (pmol)"], ["Sample 1", "Sample 2", "Sample 3", "Sample 4", "Sample 5"], ["α-synuclein (pmol)", "P37840", "1", "10", "0.5", "0.1", "5"], ["β-lactoglobulin (pmol)", "P02754", "0.5", "0.1", "5", "10", "1"], ["Fibrinogen (pmol)", "P02671, P02675, P02679", "10", "5", "1", "0.5", "0.1"], ["α, β, γ each"], ["Glucose oxidase (pmol)", "P13006", "0.1", "1", "10", "5", "0.5"], ["Hemoglobin (pmol)", "P69905, P68871", "0.5", "5", "10", "1", "0.1"], ["α, β each"], ["Lipase (pmol)", "P20261, P32946, P32947", "0.1", "0.5", "1", "5", "10"], ["1, 2, 3 each"], ["Lysozyme (pmol)", "P00698", "5", "10", "0.1", "0.5", "1"], ["Myoglobin (pmol)", "P68082", "1", "0.1", "5", "10", "0.5"], ["C2C12 lysate (μg)", "", "20", "20", "20", "20", "20"]], "row_count": 14, "column_count": 7, "numeric_cells": 60, "header_rows": 2, "locator": "section[7]/table-wrap[1]#TI"}}, {"id": "asset:table:pmid:31699904:3", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:e041fdfd9c2728fd38f951b81194a98cdbccc99e747ca84318518f83478361e5", "bytes": 1314, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[14]/table-wrap[1]#TII", "version": null, "note": "Overview of analyzed DIA libraries. The names of each library consists of the spike-in types (MM: master mix, i.e. mixture of background and spike-ins in fixed amount, GS: gold standard, i.e. the same samples as measured by DIA), the digest type (IS: in-solution, F: fragmented and therefore in-gel), whether fractionation was performed and on which level (Prot: protein fraction, Pep: peptide fractionation), finally, the number represents the number of MS runs for the library. The libraries generated by Pulsar instead of ProteomeDiscoverer are indicated by the respective prefix", "structure": {"pmcid": "PMC6944235", "section_index": 14, "section_title": "Spectral Library Generation", "row_count": 9, "column_count": 6}, "table": {"label": "Table II", "caption": "Overview of analyzed DIA libraries. The names of each library consists of the spike-in types (MM: master mix, i.e. mixture of background and spike-ins in fixed amount, GS: gold standard, i.e. the same samples as measured by DIA), the digest type (IS: in-solution, F: fragmented and therefore in-gel), whether fractionation was performed and on which level (Prot: protein fraction, Pep: peptide fractionation), finally, the number represents the number of MS runs for the library. The libraries generated by Pulsar instead of ProteomeDiscoverer are indicated by the respective prefix", "rows": [["Name", "Spike-in type", "Digest type", "Fractionation", "No of runs", "Software"], ["MM IS 6", "Constant", "In-solution", "No", "6", "PD+SN11"], ["GS IS 15", "Varying", "In-solution", "No", "15", "PD+SN11"], ["GS IS 30", "Varying", "In-solution", "No", "30", "PD+SN11"], ["MM F Prot 20", "Constant", "In-gel", "Protein", "20", "PD+SN11"], ["MM F Pep 16", "Constant", "In-solution", "Peptide", "16", "PD+SN11"], ["IS and F all 78", "Combined", "Combined", "Combined", "78", "PD+SN11"], ["Pulsar GS IS 15", "Varying", "In-solution", "No", "15", "SN Pulsar"], ["Pulsar MM F Prot 20", "Constant", "In-gel", "Protein", "20", "SN Pulsar"]], "row_count": 9, "column_count": 6, "numeric_cells": 22, "header_rows": 1, "locator": "section[14]/table-wrap[1]#TII"}}, {"id": "asset:table:pmid:31699904:4", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:379baa766afcdc138863bf98198b3ec83e815035c06f058db5576f58ebec9bc8", "bytes": 819, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[29]/table-wrap[1]#TIII", "version": null, "note": "False positive rate. Number of FP and resulting FP rate after correlation and low-fold change filtering on protein and peptide level", "structure": {"pmcid": "PMC6944235", "section_index": 29, "section_title": "Specificity", "row_count": 12, "column_count": 5}, "table": {"label": "Table III", "caption": "False positive rate. Number of FP and resulting FP rate after correlation and low-fold change filtering on protein and peptide level", "rows": [["", "Protein level", "Peptide level"], ["FP (filtered)", "FP rate", "FP (filtered)", "FP rate"], ["DDA", "5", "20.00%", "124", "42.61%"], ["DirectDIA", "13", "28.89%", "118", "35.22%"], ["MM IS 6", "8", "25.00%", "119", "40.61%"], ["GS IS 15", "5", "14.29%", "123", "34.36%"], ["GS IS 30", "6", "16.22%", "125", "32.81%"], ["MM F Prot 20", "14", "29.79%", "167", "43.04%"], ["MM F Pep 16", "12", "30.77%", "129", "43.58%"], ["IS and F all 78", "14", "28.57%", "226", "42.56%"], ["Pulsar GS IS 15", "12", "26.67%", "188", "40.26%"], ["Pulsar MM F Prot 20", "14", "30.43%", "214", "43.50%"]], "row_count": 12, "column_count": 5, "numeric_cells": 48, "header_rows": 2, "locator": "section[29]/table-wrap[1]#TIII"}}, {"id": "asset:table:pmid:31699904:5", "role": "table", "format": "jats-table", "media_type": "application/vnd.jats.table+json", "uri": null, "sha256": "sha256:dbf262192c1ccc72e11e4be1520f7bc41580036247fe4d38c82e8890bb0f6997", "bytes": 1081, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[29]/table-wrap[2]#TIV", "version": null, "note": "AUCs and mean absolute percentage errors. Table showing the AUC of the ROCs and the mean absolute percentage error (MAPE) of the respective method on the peptide and protein level. The MAPE was calculated between the median of achieved and expected protein/peptide fold changes, on log2 transformed ratios. The values can be interpreted as deviation between the achieved and expected ratio in percent", "structure": {"pmcid": "PMC6944235", "section_index": 29, "section_title": "Specificity", "row_count": 12, "column_count": 5}, "table": {"label": "Table IV", "caption": "AUCs and mean absolute percentage errors. Table showing the AUC of the ROCs and the mean absolute percentage error (MAPE) of the respective method on the peptide and protein level. The MAPE was calculated between the median of achieved and expected protein/peptide fold changes, on log2 transformed ratios. The values can be interpreted as deviation between the achieved and expected ratio in percent", "rows": [["", "Peptide", "Protein"], ["AUC", "MAPE", "AUC", "MAPE"], ["DDA", "0.61", "65.20%", "0.88", "57.77%"], ["DirectDIA", "0.92", "16.16%", "0.87", "21.67%"], ["MM IS 6", "0.85", "18.45%", "0.97", "23.15%"], ["GS IS 15", "0.92", "13.99%", "0.95", "22.84%"], ["GS IS 30", "0.88", "17.08%", "0.87", "25.92%"], ["MM F Prot 20", "0.73", "16.10%", "0.82", "21.41%"], ["MM F Pep 16", "0.68", "15.39%", "0.80", "27.21%"], ["IS and F all 78", "0.63", "15.12%", "0.93", "27.52%"], ["Pulsar GS IS 15", "0.76", "13.78%", "0.88", "17.69%"], ["Pulsar MM F Prot 20", "0.63", "12.91%", "0.94", "19.53%"]], "row_count": 12, "column_count": 5, "numeric_cells": 48, "header_rows": 2, "locator": "section[29]/table-wrap[2]#TIV"}}], "gaps": ["asset:data:geo:PXD012986 not downloaded", "asset:data:geo:PXD012986 licence not read", "asset:data:geo:PXD012987 not downloaded", "asset:data:geo:PXD012987 licence not read", "asset:data:geo:PXD012988 not downloaded", "asset:data:geo:PXD012988 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "group_comparison", "specific": "anova", "label": "Group comparison", "dsl_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "Afterward, an analysis of variance (ANOVA) model was fitted to the transformed data. As a post-hoc test Tukey's honest significance test was conducted, to determine, which spike-in states were significantly differential. Finally, the ANOVA p values were corrected for multiple testing using the Benjamini-Hochberg procedure."}}, {"id": "method-2", "family": "correlation_analysis", "specific": "pearson", "label": "Correlation analysis", "dsl_operations": ["pearson_r"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["paired_numeric_columns"], "evidence": {"score": 0.8, "band": "high", "quote": "Pearson's correlation"}}], "targets": [], "gaps": []}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: anova."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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Generation of the Gold Standard", "cribed in the prior paragraphs a total of 78 LC-MS/MS runs were analyzed for the spectral library", "t a common replicate number, resulting in 15 LC-MS/MS runs per method. For the retention time alig"], "order_basis": "paper_text_order:154", "id": "assay:1"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["10 min. As spike-in proteins we chose 13 non-mouse proteins with varying physico-chemical prope", "ate background and varying amounts of 13 non-mouse spike-in proteins. Shown are the pmol amount", "tions, which could be detected and mapped to mouse proteins. To account for these FP, the abund", "ity, specificity and accuracy, we used whole mouse cell lysate as constant background, represen"], "order_basis": "paper_text_order:3550", "id": "assay:2"}, {"role": "analysis", "definition_id": "group_comparison", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["group_summary", "welch_t", "bootstrap_mean_difference", "group_comparison_anova"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Afterward, an analysis of variance (ANOVA) model was fitted to the transformed data. As a post-hoc test Tukey's honest significance test was conducted, to determine, which spike-in states were significantly differential. Finally, the ANOVA p values were corrected for multiple testing using the Benjamini-Hochberg procedure."], "order_basis": "consumes_earlier_output", "id": "assay:3"}]}