Stable
isotope labeling by
amino acids in cell culture (SILAC) coupled to data-dependent acquisition (
DDA) is a common approach to quantitative proteomics with the desirable benefit of reducing batch effects during sample processing and data acquisition. More recently, using data-independent acquisition (
DIA/SWATH) to systematically measure
peptides has gained popularity for its comprehensiveness, reproducibility, and accuracy of quantification. The complementary advantages of these two techniques logically suggests combining them. Here we develop a SILAC-
DIA-MS workflow using free, open-source software. We empirically determine that using
DIA achieves similar
peptide detection numbers as
DDA and that
DIA improves the quantitative accuracy and precision of SILAC by an order of magnitude. Finally, we apply SILAC-
DIA-MS to determine
protein turnover rates of cells treated with
bortezomib, an FDA-approved
26S proteasome inhibitor for
multiple myeloma and
mantle cell lymphoma. We observe that SILAC-
DIA produces more sensitive
protein turnover models. Of the
proteins determined to be differentially degraded by both acquisition methods, we find known
proteins that are degraded by the
ubiquitin-
proteasome pathway, such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a
protein implicated in invasive
breast cancer. With improved quantification from
DIA, we anticipate that this workflow will make SILAC-based experiments like
protein turnover more sensitive.