Detection and quantitation of the RNA-interacting proteome is commonly achieved applying SILAC labeling, following by data-dependent acquisition (DDA) of the proteomic data. However, the limited sensitivity of the DDA approach often restricts protein detection to those with higher expression in cells, necessitating peptide fractionation prior to mass spectrometry. Here we report a pipeline for SILAC analysis using data-independent acquisition (DIA), with a spectral library constructed using gas-phase window separation of light materials followed by in silico prediction of heavy spectra. The resulting DIA datasets had 30-40% more detected proteins compared to the same biological materials analyzed using DDA, while abolishing the requirement for pre-MS reverse-phase fractionation of peptides. Lower inter-replicate variations were seen with DIA for proteins detected in both acquisitions. As a test, we determined the effects of arsenite treatment on the RNA-bound proteome of HEK cells recovered following total RNA-associated proteome purification (TRAPP). The DIA dataset yielded clear GO term enrichment for RNA-binding proteins involved in cellular stress responses, while enrichment in the DDA dataset was relatively weak. Dataset normalization of with Cyclic Loess slightly improved the DDA-DIA correlation over median normalization for DIA datasets, but not for DDA dataset relative to default MaxQuant normalization. Overall, the DIA SILAC approach improved both protein detection and biological significance over conventional DDA SILAC.