IonQuant Enables Accurate and Sensitive Label-Free Quantification With FDR-Controlled Match-Between-Runs

Fengchao Yu,Sarah E Haynes,Alexey I Nesvizhskii

doi:10.1016/j.mcpro.2021.100077

Abstract

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. Though tolerances are used to ensure such transferred identifications are reasonably located and meet certain quality thresholds, little work has been done to evaluate the statistical confidence of MBR. Here, we present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19–38 times faster; 6–18% more proteins quantified and with comparable or better accuracy). We further illustrate the performance of IonQuant and highlight the need for FDR-controlled MBR, in two single-cell proteomics experiments, including one acquired with the help of high-field asymmetric ion mobility spectrometry separation. Fully integrated in the FragPipe computational environment, IonQuant with FDR-controlled MBR enables fast and accurate peptide and protein quantification in label-free proteomics experiments.

Highlights

Missing values weaken the power of label-free quantitative proteomic experiments to uncover true quantitative differences between biological samples or experimental conditions
We present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant
Using several benchmarking datasets generated on both Orbitrap and timsTOF mass spectrometers, we demonstrate superior performance of IonQuant with FDR-controlled MBR compared with MaxQuant (19–38 times faster; 6–18% more proteins quantified and with comparable or better accuracy)

Summary

Graphical Abstract

In Brief Match-between-runs is a powerful approach to mitigate the missing value problem in label-free quantification It transfers features identified by MS/MS from one run to the other, but previously, there was no false discovery rate control over this process. Match-between-runs (MBR) has become a common approach to mitigate the missing value problem, where peptides identified by tandem mass spectra in one run are transferred to another by inference based on m/z, charge state, retention time, and ion mobility when applicable. We present a mixture model-based approach to estimate the false discovery rate (FDR) of peptide and protein identification transfer, which we implement in the label-free quantification tool IonQuant. Owing to the stochastic nature of intensity-based sampling of peptide ions for MS/MS analysis, not all peptides are consistently identified in all runs This in turn gives rise to missing quantification values, weakening essential comparisons between different biological samples or experimental conditions. We implement the new methods as a default option in IonQuant, readily available as a standalone tool or within our integrated computational platform FragPipe (https://fragpipe.nesvilab.org/)

EXPERIMENTAL PROCEDURES

RESULTS AND DISCUSSION

CONCLUSIONS