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Tissue-based absolute quantification using large-scale TMT and LFQ experiments
  • +4
  • Hong Wang,
  • Chengxin Dai,
  • Julianus Pfeuffer,
  • Timo Sachsenberg,
  • Aniel Sanchez,
  • Mingze Bai,
  • Yasset Perez Riverol
Chengxin Dai
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Julianus Pfeuffer
University of Tübingen
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Timo Sachsenberg
University of Tübingen
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Aniel Sanchez
Lund University
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Mingze Bai
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Yasset Perez Riverol
European Bioinformatics Institute

Corresponding Author:[email protected]

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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.
16 Apr 2023Submitted to PROTEOMICS
17 Apr 2023Submission Checks Completed
17 Apr 2023Assigned to Editor
17 Apr 2023Review(s) Completed, Editorial Evaluation Pending
17 Apr 2023Reviewer(s) Assigned
30 May 2023Editorial Decision: Revise Minor
19 Jun 2023Review(s) Completed, Editorial Evaluation Pending
19 Jun 20231st Revision Received
20 Jun 2023Reviewer(s) Assigned
03 Jul 2023Editorial Decision: Revise Minor
04 Jul 2023Review(s) Completed, Editorial Evaluation Pending
04 Jul 20232nd Revision Received
05 Jul 2023Editorial Decision: Accept