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Peer-reviewedCC BYJournal of Proteome Research

Recommendations for Quantitative Data-Independent Acquisition (DIA) Proteomics Using Controlled Quantitative Experiments (CQEs)

  • Andrew Michael Frey1
  • Frances R. Sidgwick1
  • Andrew Porter1
  • Paweł Palmowski1
  • Matthias Trost1,2
  1. 1Newcastle University , , ,
  2. 2University of Manchester , , ,
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Abstract

Advances in mass spectrometry and associated computational tools have resulted in the widespread adoption of Data-Independent Acquisition (DIA) for proteomics. Experiments using state-of-the-art instrumentation and specialized modes of acquisition report ever-increasing throughput and protein identification rates. However, experiments to test the efficacy of protein quantitation in these novel methods are surprisingly rare, especially in novel methods where low sample input or high throughput is considered. Here, we performed a series of controlled quantitative experiments (CQEs) on two commonly used mass spectrometry platforms (Exploris480 and timsTOF HT), using defined mixtures of human, yeast, and bacterial proteomes. Peptide spectrum matching and initial quantification were performed using DIA-NN, with a series of postprocessing options also compared. Overall, we identified >10,000 unique proteins and >160,000 precursors across samples with excellent accuracy and precision. Additional postprocessing was found to have drastic impacts on ID rates, quantitative precision and accuracy, and false quantitative rate (FQR). These impacts were particularly notable in experiments using low amounts of sample. In optimal loading conditions, data processing could reduce FQR from ∼0.1 to 0% with minimal losses in ID rates. Ultimately, we present a series of recommendations for proteomics data processing and advice on the use of CQEs and resulting metrics when assessing quantitative efficacy in novel modes of acquisition.

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Cite

BibTeXRIS
BibTeX
@article{frey2026recommendations,
  title = {{Recommendations for Quantitative Data-Independent Acquisition (DIA) Proteomics Using Controlled Quantitative Experiments (CQEs)}},
  author = {Andrew Michael Frey and Frances R. Sidgwick and Andrew Porter and Paweł Palmowski and Matthias Trost},
  journal = {Journal of Proteome Research},
  year = {2026},
  month = oct,
  doi = {10.1021/acs.jproteome.6c00328},
  url = {https://doi.org/10.1021/acs.jproteome.6c00328},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Recommendations for Quantitative Data-Independent Acquisition (DIA) Proteomics Using Controlled Quantitative Experiments (CQEs)
AU  - Andrew Michael Frey
AU  - Frances R. Sidgwick
AU  - Andrew Porter
AU  - Paweł Palmowski
AU  - Matthias Trost
PY  - 2026
DA  - 2026/10/22
JO  - Journal of Proteome Research
DO  - 10.1021/acs.jproteome.6c00328
UR  - https://doi.org/10.1021/acs.jproteome.6c00328
AB  - Advances in mass spectrometry and associated computational tools have resulted in the widespread adoption of Data-Independent Acquisition (DIA) for proteomics. Experiments using state-of-the-art instrumentation and specialized modes of acquisition report ever-increasing throughput and protein identification rates. However, experiments to test the efficacy of protein quantitation in these novel methods are surprisingly rare, especially in novel methods where low sample input or high throughput is considered. Here, we performed a series of controlled quantitative experiments (CQEs) on two commonly used mass spectrometry platforms (Exploris480 and timsTOF HT), using defined mixtures of human, yeast, and bacterial proteomes. Peptide spectrum matching and initial quantification were performed using DIA-NN, with a series of postprocessing options also compared. Overall, we identified >10,000 unique proteins and >160,000 precursors across samples with excellent accuracy and precision. Additional postprocessing was found to have drastic impacts on ID rates, quantitative precision and accuracy, and false quantitative rate (FQR). These impacts were particularly notable in experiments using low amounts of sample. In optimal loading conditions, data processing could reduce FQR from ∼0.1 to 0% with minimal losses in ID rates. Ultimately, we present a series of recommendations for proteomics data processing and advice on the use of CQEs and resulting metrics when assessing quantitative efficacy in novel modes of acquisition.
SN  - 1535-3893
SN  - 1535-3907
ER  -