Improved quantitative accuracy in data-independent acquisition proteomics via retention time boundary imputation

Area of Science:

• Proteomics
• Biotechnology
• Analytical Chemistry

Background:

• Missing values are a challenge in DIA proteomics, with traditional methods like protein removal or statistical imputation having limitations.
• Protein removal reduces statistical power, while imputation can introduce bias and obscure true biological signals.

Purpose of the Study:

• To develop a novel imputation method for DIA proteomics that overcomes the limitations of existing approaches.
• To improve the accuracy of peptide quantification and increase statistical power in proteomic analyses.

Main Methods:

• A new approach, Nettle, imputes peptide retention times (RTs) instead of direct quantifications.
• Missing values are handled by imputing RT boundaries, followed by signal integration within these boundaries for quantification.

Main Results:

• Nettle provides more accurate quantitations compared to existing DIA proteomics imputation methods.
• The method successfully identified differentially abundant peptides related to Alzheimer's disease genes, which were missed by library search alone.
• RT boundary imputation enhances the estimation of radiation exposure in biological tissues and improves quantification of low-abundance peptides.

Conclusions:

• Nettle significantly increases the number of quantifiable peptides, thereby boosting statistical power in DIA proteomics studies.
• This RT boundary imputation method offers a more robust and accurate approach to handling missing data, particularly for low-abundance peptides.
• Nettle is available as a standalone tool, facilitating its adoption in proteomic research.