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| Main Authors: | , , , , , , , , , , |
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| Format: | Artículo científico |
| Language: | en |
| Published: |
Proteomics
2025
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| Subjects: | |
| Online Access: | https://pubmed.ncbi.nlm.nih.gov/40211604/ |
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| _version_ | 1868266216501542913 |
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| author | Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D |
| author_facet | Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D |
| collection | PubMed - marine biology |
| contents | Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D Proteomics Microbiota Mass Spectrometry Peptides Reproducibility of Results Software Proteome Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being data-dependent acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-independent acquisition mass spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. In this study, DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory for the particular instruments and software parameters chosen. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation. |
| format | Artículo científico |
| id | pubmed_40211604 |
| institution | PubMed |
| language | en |
| publishDate | 2025 |
| publisher | Proteomics |
| record_format | pubmed |
| spellingShingle | Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D Proteomics Microbiota Mass Spectrometry Peptides Reproducibility of Results Software Proteome Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. Rajczewski, Andrew T Blakeley-Ruiz, J Alfredo Meyer, Annaliese Vintila, Simina McIlvin, Matthew R Van Den Bossche, Tim Searle, Brian C Griffin, Timothy J Saito, Mak A Kleiner, Manuel Jagtap, Pratik D Proteomics Microbiota Mass Spectrometry Peptides Reproducibility of Results Software Proteome Mass spectrometry (MS)-based metaproteomics is used to identify and quantify proteins in microbiome samples, with the frequently used methodology being data-dependent acquisition mass spectrometry (DDA-MS). However, DDA-MS is limited in its ability to reproducibly identify and quantify lower abundant peptides and proteins. To address DDA-MS deficiencies, proteomics researchers have started using Data-independent acquisition mass spectrometry (DIA-MS) for reproducible detection and quantification of peptides and proteins. We sought to evaluate the reproducibility and accuracy of DIA-MS metaproteomic measurements relative to DDA-MS using a mock community of known taxonomic composition. Artificial microbial communities of known composition were analyzed independently in three laboratories using DDA- and DIA-MS acquisition methods. In this study, DIA-MS yielded more protein and peptide identifications than DDA-MS in each laboratory for the particular instruments and software parameters chosen. In addition, the protein and peptide identifications were more reproducible in all laboratories and provided an accurate quantification of proteins and taxonomic groups in the samples. We also identified some limitations of current DIA tools when applied to metaproteomic data, highlighting specific needs to improve DIA tools enabling analysis of metaproteomic datasets from complex microbiomes. Ultimately, DIA-MS represents a promising strategy for MS-based metaproteomics due to its large number of detected proteins and peptides, reproducibility, deep sequencing capabilities, and accurate quantitation. |
| title | Data-Independent Acquisition Mass Spectrometry as a Tool for Metaproteomics: Interlaboratory Comparison Using a Model Microbiome. |
| topic | Proteomics Microbiota Mass Spectrometry Peptides Reproducibility of Results Software Proteome |
| url | https://pubmed.ncbi.nlm.nih.gov/40211604/ |