Dynamic 3D-Network Coating Composite Enables Global Isolation of Phosphopeptides, Stepwise Separation of Mono- and Multi-Phosphopeptides, and Phosphoproteomics of Human Lung Cells.

Abstract

Protein phosphorylation is one of the most common and important post-translational modifications (PTMs) and is highly involved in various biological processes. Ideal adsorbents with high sensitivity and specificity toward phosphopeptides with large coverage are therefore essential for enrichment and mass spectroscopy-based phosphoproteomics analysis. In this study, a newly designed IMAC adsorbent composite was constructed on the graphene matrix coated with mesoporous silica. The outer functional 3D-network layer was prepared by free radical polymerization of the phosphonate-functionalized vinyl imidazolium salt monomer and subsequent metal immobilization. Due to its unique structural feature and high content of Ti⁴⁺ ions, the resulting phosphonate-immobilized adsorbent composite G@mSiO₂@PPFIL-Ti⁴⁺ exhibits excellent performance in phosphopeptide enrichment with a low detection limit (0.1 fmol, tryptic β-casein digest) and superior selectivity (molar ratio of 1:15,000, digest mixture of β-casein and bovine serum albumin). G@mSiO₂@PPFIL-Ti⁴⁺ displays high tolerance to loading and elution conditions and thus can be reused without a marked decrease in enrichment efficacy. The captured phosphopeptides can be released globally, and mono-/multi-phosphopeptides can be isolated stepwise by gradient elution. When applying this material to enrich phosphopeptides from human lung cell lysates, a total of 3268 unique phosphopeptides were identified, corresponding to 1293 phosphoproteins. Furthermore, 2698 phosphorylated peptides were found to be differentially expressed (p < 0.05) between human lung adenocarcinoma cells (SPC-A1) and human normal epithelial cells (Beas-2B), of which 1592 were upregulated and 1106 were downregulated in the cancer group. These results demonstrate the material's superior enrichment efficiency in complex biological samples.