Biocompatible and Antifouling Linear Poly(N‑(2-hydroxypropyl)methacrylamide)-Coated Capillaries via Aqueous RAFT Polymerization Method for Clinical Proteomics Analysis of Non-Small Cell Lung Cancer Tissue by CZE-ESI-MS/MS

Capillary coating plays a crucial role in the separation efficiency and reproducibility of capillary zone electrophoresis (CZE). In this study, a linear poly(N-(2-hydroxypropyl)methacrylamide) (LP(HPMA))-coated capillary was prepared by using the surface-confined aqueous reversible addition–fragmentation chain transfer polymerization method. The LP(HPMA)-coated capillary exhibits better biocompatibility and stability compared with the linear poly(acrylamide) (LPA)-coated capillary. Through systematic evaluation, the optimal conditions for fabricating LP(HPMA)-coated capillaries were determined as follows: 1 M HPMA, 2.7 × 10–3 M 4,4′-azobis(4-cyanovaleric acid), and a reaction time of 4 h. LP(HPMA)-coated capillaries prepared under these conditions exhibited the lowest electro-osmotic flow values of 3.7 × 10–6 and demonstrated exceptional performance when applied to the analysis of a HeLa cell digest. Following a 20-day treatment with 2 M NH4OAc (pH 7), the number of peptides identified using the LP(HPMA)-coated capillary decreased by 18.1% compared to the untreated LP(HPMA)-coated capillary; in contrast, the peptide numbers for the LPA-coated capillary decreased by 26.7% relative to its untreated counterpart. These results indicate that the LP(HPMA)-coated capillary exhibits superior pH stability compared to the LPA-coated capillary within the physiological pH range. The LP(HPMA)-coated capillary was employed in the clinical proteomics analysis of non-small cell lung cancer tissues and their corresponding paracancerous tissues via CZE-ESI-MS/MS. The differentially expressed proteins in each sample pair demonstrated minimal overlap, highlighting the heterogeneity of tumor tissues among different patients. Principal component analysis of the data could segregate the samples into a tumor tissue cluster and a paracancerous tissue cluster. Moreover, within each cluster, samples from different patients further separated into subclusters. This discovery validates that CZE-MS can not only distinguish between lung cancer tumor tissue and paracancerous tissue but also detect the heterogeneity among diverse patients, even with as little as 100 ng of sample. The obtained results were subsequently compared with those from nanoRPLC-MS using 1 μg of sample. Notably, the enrichment results within the three gene ontology categories for both methods showed a high level of consistency, corroborating the effectiveness of the CZE-MS method using an LP(HPMA)-coated capillary in identifying differentially expressed proteins from mass-limited clinical samples.