Data-independent acquisition (DIA) has recently emerged as a powerful quantitative approach for large-scale proteome quantification, providing a sensitive and reproducible alternative to data-dependent acquisition (DDA). However, lack of compatible multiplexed quantification methods is a bottleneck of DIA. To alleviate this challenge, we present a mass defect based four-plex data-independent acquisition strategy, termed "MdFDIA", for parallel analysis of four different protein samples in a DIA experiment without the additional complexity of tandem mass spectrometry (MS²) spectra. MdFDIA is a hybrid approach that combines stable isotope labeling with amino acids in cell culture (SILAC) and dimethyl labeling. Briefly, the isotopes ¹³C₆¹⁵N₂-lysine (+8.0142 Da, light) and D₈-lysine (+8.0512 Da, heavy) were metabolically embedded in different proteome samples during cell culture. Then, two ¹³C₆¹⁵N₂-lysine and D₈-lysine labeled protein samples were digested with Lys-C, followed by in vitro labeling with light (2¹³CD₂H, +34.06312 Da) and heavy (2CD₃, +34.06896 Da) dimethyl groups, respectively, producing four different pseudoisobaric labeled protein samples. The labeled samples were then equally mixed and analyzed by DIA. The subtle mass differences between the four labeled forms in MS² scans can be resolved on an Orbitrap Fusion Lumos instrument to facilitate quantification without abundance information encoded in MS² spectra. Additionally, a systematic investigation was carried out and revealed that MdFDIA enabled a significant decrease of the adverse impact on the accuracy of the quantitative assays arising from the chromatographic isotope effect, especially the deuterium effect, which typically occurs in a DDA experiment. Additionally, MdFDIA provided a method for validating the fragment type in the DIA spectra identification result. Furthermore, MdFDIA was applied to quantitative proteome analyses of four different breast cancer cell lines, demonstrating the feasibility of this strategy for biological applications.

#	Concept	H-Index	Publications	Citations
1	Medicine	4	4	193
2	Natural Sciences	4	4	193
3	Molecular Biology	4	4	193
4	Biochemistry	3	3	154
5	Cell Biology	1	1	39

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