Tissue-based absolute quantification using large-scale TMT and LFQ experiments
(2023) In Proteomics 23(20).- Abstract
Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same... (More)
Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.
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- author
- Wang, Hong ; Dai, Chengxin ; Pfeuffer, Julianus ; Sachsenberg, Timo ; Sanchez, Aniel LU ; Bai, Mingze and Perez-Riverol, Yasset
- organization
- publishing date
- 2023
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- absolute protein expression, big data, LFQ, proteomics data reanalysis, public data, TMT
- in
- Proteomics
- volume
- 23
- issue
- 20
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- pmid:37488995
- scopus:85165602080
- ISSN
- 1615-9853
- DOI
- 10.1002/pmic.202300188
- language
- English
- LU publication?
- yes
- id
- b3d079e4-68a2-4662-b708-6d4cebf2aa74
- date added to LUP
- 2023-09-20 11:19:42
- date last changed
- 2024-04-19 01:10:24
@article{b3d079e4-68a2-4662-b708-6d4cebf2aa74, abstract = {{<p>Relative and absolute intensity-based protein quantification across cell lines, tissue atlases and tumour datasets is increasingly available in public datasets. These atlases enable researchers to explore fundamental biological questions, such as protein existence, expression location, quantity and correlation with RNA expression. Most studies provide MS1 feature-based label-free quantitative (LFQ) datasets; however, growing numbers of isobaric tandem mass tags (TMT) datasets remain unexplored. Here, we compare traditional intensity-based absolute quantification (iBAQ) proteome abundance ranking to an analogous method using reporter ion proteome abundance ranking with data from an experiment where LFQ and TMT were measured on the same samples. This new TMT method substitutes reporter ion intensities for MS1 feature intensities in the iBAQ framework. Additionally, we compared LFQ-iBAQ values to TMT-iBAQ values from two independent large-scale tissue atlas datasets (one LFQ and one TMT) using robust bottom-up proteomic identification, normalisation and quantitation workflows.</p>}}, author = {{Wang, Hong and Dai, Chengxin and Pfeuffer, Julianus and Sachsenberg, Timo and Sanchez, Aniel and Bai, Mingze and Perez-Riverol, Yasset}}, issn = {{1615-9853}}, keywords = {{absolute protein expression; big data; LFQ; proteomics data reanalysis; public data; TMT}}, language = {{eng}}, number = {{20}}, publisher = {{John Wiley & Sons Inc.}}, series = {{Proteomics}}, title = {{Tissue-based absolute quantification using large-scale TMT and LFQ experiments}}, url = {{http://dx.doi.org/10.1002/pmic.202300188}}, doi = {{10.1002/pmic.202300188}}, volume = {{23}}, year = {{2023}}, }