Purpose: Clinical deployment of multi-omics demands rapid, reproducible, and scalable sample preparation without consuming scarce tissue in separate workflows. Si-Trap (Simultaneous Trapping), a development over S-Trap, yields proteomic, lipidomic and metabolomic fractions from the same specimen while preserving biological concordance across omes.
Methods: Cells or tissues are lysed in detergent-free aqueous, acidic, or basic conditions. Proteins are captured on Si-Trap loose spin columns or automated 96-well plates (processing 4–96 samples in parallel); on-matrix reduction/alkylation is performed in situ, followed by a 1 h trypsin digest at 47 °C and peptide elution for LC-MS. The detergent-free flow-through is used for targeted metabolomics and lipidomics (e.g., acylcarnitines, free fatty acids, bile acids). We benchmarked Si-Trap versus S-Trap across protein identifications and cellular-component coverage, FFPE compatibility, denaturing and native modes, intra-/inter-run CV distributions, and coordinated proteomic–metabolomic changes in clear-cell renal carcinoma (ccRCC) vs adjacent normal kidney. Automation was implemented on a low-cost Tecan A200 positive-pressure workstation.
Results: Si-Trap matched S-Trap for protein depth and coverage, and sampled all cellular compartments reproducibly in both denaturing and native modes, including robust FFPE performance. In short-run analyses using MDA-MB-231 cells, Si-Trap delivered ~1,278–1,293 ≥2-peptide protein IDs with comparable ID rates and unbiased GO cellular-component profiles versus SDS/S-Trap controls. Precision was high: across all quantified analytes (proteins, metabolites and lipids), >50% of CVs were <10% and >67% were <15%. In paired ccRCC analyses (5% FDR), metabolomics revealed decreased short-chain acylcarnitines (C5, C5:1, C3) and reduced PUFAs (C20:5, C20:4, C22:6) in tumors, mirrored by proteomic down-regulation of carnitine/PUFA-pathway enzymes (CRAT, CPT2, CPT1A, ACOT1, ACSL1).
Conclusion: Si-Trap provides minutes-scale, automation-ready preparation of matched proteomic, metabolomic and lipidomic fractions from the same sample, with low cost of implementation, excellent reproducibility, and compatibility with FFPE samples, thus enabling scalable clinical and translational multi-omics.