Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment
Abstract DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2023-10-01
|
| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-023-05437-2 |
| _version_ | 1797636770431500288 |
|---|---|
| author | Shubham Gupta Justin C. Sing Hannes L. Röst |
| author_facet | Shubham Gupta Justin C. Sing Hannes L. Röst |
| author_sort | Shubham Gupta |
| collection | DOAJ |
| description | Abstract DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment. |
| first_indexed | 2024-03-11T12:38:54Z |
| format | Article |
| id | doaj.art-a98ebd438962461d9d19ed06dd776dc6 |
| institution | Directory Open Access Journal |
| issn | 2399-3642 |
| language | English |
| last_indexed | 2024-03-11T12:38:54Z |
| publishDate | 2023-10-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj.art-a98ebd438962461d9d19ed06dd776dc62023-11-05T12:26:54ZengNature PortfolioCommunications Biology2399-36422023-10-016111210.1038/s42003-023-05437-2Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignmentShubham Gupta0Justin C. Sing1Hannes L. Röst2Terrence Donnelly Centre for Cellular & Biomolecular Research, University of TorontoTerrence Donnelly Centre for Cellular & Biomolecular Research, University of TorontoTerrence Donnelly Centre for Cellular & Biomolecular Research, University of TorontoAbstract DIA is a mainstream method for quantitative proteomics, but consistent quantification across multiple LC-MS/MS instruments remains a bottleneck in parallelizing data acquisition. One reason for this inconsistency and missing quantification is the retention time shift which current software does not adequately address for runs from multiple sites. We present multirun chromatogram alignment strategies to map peaks across columns, including the traditional reference-based Star method, and two novel approaches: MST and Progressive alignment. These reference-free strategies produce a quantitatively accurate data-matrix, even from heterogeneous multi-column studies. Progressive alignment also generates merged chromatograms from all runs which has not been previously achieved for LC-MS/MS data. First, we demonstrate the effectiveness of multirun alignment strategies on a gold-standard annotated dataset, resulting in a threefold reduction in quantitation error-rate compared to non-aligned DIA results. Subsequently, on a multi-species dataset that DIAlignR effectively controls the quantitative error rate, improves precision in protein measurements, and exhibits conservative peak alignment. We next show that the MST alignment reduces cross-site CV by 50% for highly abundant proteins when applied to a dataset from 11 different LC-MS/MS setups. Finally, the reanalysis of 949 plasma runs with multirun alignment revealed a more than 50% increase in insulin resistance (IR) and respiratory viral infection (RVI) proteins, identifying 11 and 13 proteins respectively, compared to prior analysis without it. The three strategies are implemented in our DIAlignR workflow (>2.3) and can be combined with linear, non-linear, or hybrid pairwise alignment.https://doi.org/10.1038/s42003-023-05437-2 |
| spellingShingle | Shubham Gupta Justin C. Sing Hannes L. Röst Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment Communications Biology |
| title | Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment |
| title_full | Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment |
| title_fullStr | Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment |
| title_full_unstemmed | Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment |
| title_short | Achieving quantitative reproducibility in label-free multisite DIA experiments through multirun alignment |
| title_sort | achieving quantitative reproducibility in label free multisite dia experiments through multirun alignment |
| url | https://doi.org/10.1038/s42003-023-05437-2 |
| work_keys_str_mv | AT shubhamgupta achievingquantitativereproducibilityinlabelfreemultisitediaexperimentsthroughmultirunalignment AT justincsing achievingquantitativereproducibilityinlabelfreemultisitediaexperimentsthroughmultirunalignment AT hanneslrost achievingquantitativereproducibilityinlabelfreemultisitediaexperimentsthroughmultirunalignment |