Project description: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 quantitative techniques logically suggests combining them. Here, we develop a SILAC-DIA-MS workflow using free, open-source software. We determine empirically 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 proteasome inhibitor for multiple myeloma and mantle cell lymphoma, where we observe that SILAC-DIA produces more sensitive protein turnover models. Of the proteins determined differentially degraded by both acquisition methods, we find known ubiquitin-proteasome degrands such as HNRNPK, EIF3A, and IF4A1/EIF4A-1, and a slower turnover for CATD, a protein implicated in invasive breast cancer. With improved comprehensive quantification from DIA, we anticipate making SILAC-based experiments more sensitive and reproducible, especially pulse chase SILAC for protein turnover.
Project description:To measure translational efficiency in FMRP depletion, we purified RNAs from either wild-type or FMR1-knockout (FMR1-KO) SH-SY5Y cells generated for SILAC coupled to LC-MS/MS analysis and performed RNA-seq to quantitate mRNA abundance to normalize their protein abundance.
Project description:The data-independent acquisition (DIA) mode performed in the latest high-resolution, high-speed mass spectrometers offers a powerful analytical tool for biological investigations. The DIA mass spectrometry (MS) combined with the isotopic labeling approach holds a particular promise for increasing the multiplexity of DIA-MS analysis, which could assist the relative protein quantification and the proteome-wide turnover profiling. However, the wide isolation windows employed in conventional DIA methods lead to a limited efficiency in identifying and quantifying isotope-labelled peptide pairs. Here, we optimized a high-selectivity DIA-MS named BoxCarmax that supports the analysis of complex samples, such as those generated from Stable isotope labeling by amino acids in cell culture (SILAC) and pulse SILAC (pSILAC) experiments. BoxCarmax enables multiplexed acquisition at both MS1- and MS2- levels, through the integration of BoxCar and MSX features, as well as a gas-phase separation strategy. We found BoxCarmax modestly increased the identification rate for label-free and labeled samples but significantly improved the quantitative accuracy in SILAC and pSILAC samples. We further applied BoxCarmax in studying the protein degradation regulation during serum starvation stress in cultured cells, revealing valuable biological insights. Our study offered a new and accurate approach for the MS analysis of protein turnover and complex samples.
Project description: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.
Project description:Data Independent Acquisition (DIA) is increasingly preferred over Data Dependent Acquisition (DDA) due to its higher throughput and fewer missing values. Whereas DDA often utilizes stable isotope labeling to improve quantification, DIA mostly relies on label-free approaches. Efforts to integrate DIA with isotope labeling include chemical methods like mTRAQ and dimethyl labeling, which, while effective, complicate sample preparation. Stable isotope labeling by amino acids in cell culture (SILAC) achieves high labeling efficiency through the metabolic incorporation of heavy labels into proteins in vivo. However, the need for metabolic incorporation limits the direct use in clinical scenarios. Spike-in SILAC methods utilize an externally generated heavy sample as an internal reference, enabling SILAC-based quantification even for samples that cannot be directly labeled. Here, we combine DIA with spike-in SILAC (DIA-SiS), leveraging the robust quantification of SILAC without the complexities associated with chemical labeling. We developed and rigorously validated DIA-SiS through a mixed-species benchmark to assess its performance in proteome coverage and quantification. We demonstrate that DIA-SiS significantly improves proteome coverage and quantification compared to label-free approaches and reduces the incidence of incorrectly quantified proteins. Additionally, DIA-SiS proves effective in analyzing proteins in low-input formalin-fixed paraffin-embedded (FFPE) tissue sections. DIA-SiS combines the precision of stable isotope-based quantification with the simplicity of label-free sample preparation, facilitating simple, accurate and comprehensive proteome profiling.
Project description:We performed data independent acquisition (DIA)-based proteomics to characterize the proteomes of 67 PDAC resection specimens. Patients received either neoadjuvant chemotherapy or neoadjuvant combined chemo-radiation therapy. We employed DIA, yielding a proteome coverage in excess of 3,500 proteins. The two neoadjuvant therapies yielded highly distinguishable proteome profiles of the residual tumor mass.