DIA-SIFT: A Precursor and Product Ion Filter for Accurate Stable Isotope Data-Independent Acquisition Proteomics

Quantitative mass spectrometry-based protein profiling is widely used to measure protein levels across different treatments or disease states, yet current mass spectrometry acquisition methods present distinct limitations. While data-independent acquisition (DIA) bypasses the stochastic nature of data-dependent acquisition (DDA), fragment spectra derived from DIA are often complex and challenging to deconvolve. In-line ion mobility separation (IMS) adds an additional dimension to increase peak capacity for more efficient product ion assignment. As a similar strategy to sequential window acquisition methods (SWATH), IMS-enabled DIA methods rival DDA methods for protein annotation. Here we evaluate IMS-DIA quantitative accuracy using stable isotope labeling by amino acids in cell culture (SILAC). Since SILAC analysis doubles the sample complexity, we find that IMS-DIA analysis is not sufficiently accurate for sensitive quantitation. However, SILAC precursor pairs share common retention and drift times, and both species cofragment to yield multiple quantifiable isotopic <i>y</i>-ion peak pairs. Since <i>y</i>-ion SILAC ratios are intrinsic for each quantified precursor, combined MS1 and <i>y</i>-ion ratio analysis significantly increases the total number of measurements. With increased sampling, we present DIA-SIFT (<i>S</i>ILAC <i>I</i>ntrinsic <i>F</i>iltering <i>T</i>ool), a simple statistical algorithm to identify and eliminate poorly quantified MS1 and/or MS2 events. DIA-SIFT combines both MS1 and <i>y</i>-ion ratios, removes outliers, and provides more accurate and precise quantitation (