Multi-batch TMT reveals false positives, batch effects and missing values

Alejandro Brenes Murillo, Jens Hukelmann, Dalila Bensaddek, Angus Lamond (Lead / Corresponding author)

Research output: Contribution to journalArticle

6 Citations (Scopus)


Multiplexing strategies for large-scale proteomic analyses have become increasingly prevalent, Tandem Mass Tags (TMT) in particular. Here we used a large iPSC proteomic experiment with twenty-four 10-plex TMT batches to evaluate the effect of integrating multiple TMT batches within a single analysis. We identified a significant inflation rate of protein missing values as multiple batches are integrated and show that this pattern is aggravated at the peptide level. We also show that without normalisation strategies to address the batch effects, the high precision of quantitation within a single multiplexed TMT batch is not reproduced when data from multiple TMT batches are integrated.

Furthermore, the incidence of false positives was studied by using Y chromosome peptides as an internal control. The iPSC lines quantified in this dataset were derived from both male and female donors, hence the peptides mapped to the Y chromosome should be absent from female lines. Nonetheless, these Y chromosome-specific peptides were consistently detected in the female channels of all TMT batches. We then used the same Y chromosome specific peptides to quantify the level of ion co-isolation as well as the effect of primary and secondary reporter ion interference. These results were used to propose solutions to mitigate the limitations of multi-batch TMT analyses. We confirm that including a common reference line in every batch increases precision by facilitating normalisation across the batches and we propose experimental designs that minimise the effect of cross population reporter ion interference.
Original languageEnglish
JournalMolecular and Cellular Proteomics
Early online date22 Jul 2019
Publication statusE-pub ahead of print - 22 Jul 2019



  • proteomics
  • TMT
  • mass spectrometry
  • isobaric tags
  • bioinformatics
  • data analysis
  • missing values
  • false positives

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