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SimPLIT: Simplified sample preparation for large-scale isobaric tagging proteomics

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NIAID Data Ecosystem2026-03-13 收录
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https://www.omicsdi.org/dataset/pride/PXD031510
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Large scale proteomic profiling of cell lines can yield valuable insights into the molecular signatures attributed to variable genotypes or induced perturbations. Specifically, the ability to perform deep and rapid proteome analysis of pharmacologically modulated cells could generate drug-protein associations for large libraries of compounds that predict mechanism of action and enable rational drug design. Although isobaric labelling has greatly increased the throughput of proteomic analysis at deep coverage, the commonly used sample preparation workflows often require complex time-consuming steps and/or costly consumables, limiting their suitability for large scale studies. Here, we present a simplified and cost effective one-pot reaction sample preparation workflow in a 96-well plate format with manual parallel processing (SimPLIT), that minimizes processing steps and reduces technical variability. The workflow is based on a sodium deoxycholate lysis buffer and a single detergent clean-up step after peptide labeling, followed by quick off-line fractionation and MS2 analysis. The simplified workflow demonstrates high reproducibility and provides improved proteome representation compared to alternative approaches. We showcase the large-scale applicability of the workflow by investigating proteomic heterogeneity in a panel of colorectal cancer cell lines and by performing target discovery for a set of molecular glue degraders in different cell lines, in a 96-sample assay. Using this workflow, we report a subset of frequently dysregulated proteins in colorectal cancer cells and uncover cell-dependent protein degradation profiles of seven cereblon E3 ligase modulators (CRL4CRBN). Overall, SimPLIT is a robust method that can be easily implemented in most proteomics laboratories for medium-to-large scale TMT-based studies involving deep profiling of cell lines.
创建时间:
2022-08-03
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