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The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics
The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filte...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000Research
2014
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983906/ https://www.ncbi.nlm.nih.gov/pubmed/24741437 http://dx.doi.org/10.12688/f1000research.2-272.v2 |
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author | Vowinckel, Jakob Capuano, Floriana Campbell, Kate Deery, Michael J. Lilley, Kathryn S. Ralser, Markus |
author_facet | Vowinckel, Jakob Capuano, Floriana Campbell, Kate Deery, Michael J. Lilley, Kathryn S. Ralser, Markus |
author_sort | Vowinckel, Jakob |
collection | PubMed |
description | The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides. |
format | Online Article Text |
id | pubmed-3983906 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2014 |
publisher | F1000Research |
record_format | MEDLINE/PubMed |
spelling | pubmed-39839062014-04-15 The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics Vowinckel, Jakob Capuano, Floriana Campbell, Kate Deery, Michael J. Lilley, Kathryn S. Ralser, Markus F1000Res Method Article The combination of qualitative analysis with label-free quantification has greatly facilitated the throughput and flexibility of novel proteomic techniques. However, such methods rely heavily on robust and reproducible sample preparation procedures. Here, we benchmark a selection of in gel, on filter, and in solution digestion workflows for their application in label-free proteomics. Each procedure was associated with differing advantages and disadvantages. The in gel methods interrogated were cost effective, but were limited in throughput and digest efficiency. Filter-aided sample preparations facilitated reasonable processing times and yielded a balanced representation of membrane proteins, but led to a high signal variation in quantification experiments. Two in solution digest protocols, however, gave optimal performance for label-free proteomics. A protocol based on the detergent RapiGest led to the highest number of detected proteins at second-best signal stability, while a protocol based on acetonitrile-digestion, RapidACN, scored best in throughput and signal stability but came second in protein identification. In addition, we compared label-free data dependent (DDA) and data independent (SWATH) acquisition on a TripleTOF 5600 instrument. While largely similar in protein detection, SWATH outperformed DDA in quantification, reducing signal variation and markedly increasing the number of precisely quantified peptides. F1000Research 2014-04-07 /pmc/articles/PMC3983906/ /pubmed/24741437 http://dx.doi.org/10.12688/f1000research.2-272.v2 Text en Copyright: © 2014 Vowinckel J et al. http://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/publicdomain/zero/1.0/ Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication). |
spellingShingle | Method Article Vowinckel, Jakob Capuano, Floriana Campbell, Kate Deery, Michael J. Lilley, Kathryn S. Ralser, Markus The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title | The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title_full | The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title_fullStr | The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title_full_unstemmed | The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title_short | The beauty of being (label)-free: sample preparation methods for SWATH-MS and next-generation targeted proteomics |
title_sort | beauty of being (label)-free: sample preparation methods for swath-ms and next-generation targeted proteomics |
topic | Method Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3983906/ https://www.ncbi.nlm.nih.gov/pubmed/24741437 http://dx.doi.org/10.12688/f1000research.2-272.v2 |
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