Abstract
Mass spectrometry (MS) is indispensable for high-throughput quantitation of protein expression. But protein function is regulated by factors beyond abundance alone. Here, we evaluate two supercharging reagents, dimethyl sulfoxide (DMSO) and m-nitrobenzyl alcohol (mNBA), in narrow-window data-independent acquisition (nDIA)-MS. DMSO markedly enhances MS signal and protein identification, whereas mNBA primarily increases peptide identifications. Optimizating nDIA-MS with 3% DMSO boosts signal intensity by up to 56%, enabling identification of ~9,600 proteins from 1 µg HeLa digest in 15 min. Using this methodology, we quantify solubility and abundance changes in 8,694 proteins across three cell lines following short-term treatment with the proteasome inhibitor MG132 and the SUMO-activating enzyme inhibitor ML-792. MG132 affects the solubility of 1,723 proteins and the abundance of 374, and ML-792 affects 1,294 and 288, respectively. The drugs elicit distinct and sometimes opposing solubility shifts; for instance, MG132 insolubilizes HSF1, ML-792 solubilizes SP100 and insolubilizes PLOR3G, and SMAD2 shows opposite responses to those two treatments. These results reveal widespread, drug-induced remodeling of the protein solubility landscape and establish solubility profiling by nDIA-MS as a broadly applicable platform for uncovering protein state transitions and cellular responses to perturbation.
Data availability
All data generated in this study are available in the main text or the supplementary materials. Source Data are provided with this paper. The mass spectrometry proteomics data for supercharging reagents evaluation and solubility proteomics have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD064180 (supercharging reagents evaluation) and PXD064185 (solubility proteomics). The mass spectrometry data for preliminary instrument parameter optimization, evaluation, and Thermal Proteome Profiling (TPP) have been deposited to the MassIVE repository (https://massive.ucsd.edu) with the dataset identifier MSV000099787 (PXD070462) [https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID = PXD070462] (Thermal Proteome Profiling), MSV000099789 (PXD070463) [https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID = PXD070463] (instrument parameter optimizatiom), MSV000099791 (PXD070464) [https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID = PXD070464] (instrument parameter evaluation). Source data are provided with this paper.
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Acknowledgements
We thank all members of MD Anderson Proteomics Core Facility and Metabolomics Core Facility for their help and constructive discussions. This work was supported by NIH grant number 1S10OD012304-01, NIH/NCI grant number P30CA016672, and the University of Texas MD Anderson Cancer Center.
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Conceptualization: Y.X., H.Z., P.L.L. Methodology: Y.X., and H.Z. Investigation: Y.X., H.Z., L.T. and B.W. Visualization: Y.X., and H.Z. Supervision: P.L.L. and J.N.W. Writing—original draft: Y.X., and H.Z. Writing—review and editing: Y.X., H.Z., P.L.L. and J.N.W.
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Xiong, Y., Zhang, H., Tan, L. et al. Supercharging-enhanced nDIA-MS enables global profiling of drug-induced proteome solubility shifts. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69025-8
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DOI: https://doi.org/10.1038/s41467-026-69025-8
