Improved SILAC Quantification with Data-Independent Acquisition to Investigate Bortezomib-Induced Protein Degradation

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.