Abstract
BackgroundPlasma is ideal for early detection of cancer because samples are easily available by less invasive methods. The considerable complexities of clinical samples (high-abundant proteins, protein concentration, and dynamic range) make it extremely difficult to identify proteins of interest. Present plasma protein profiling strategies use immunodepletion to remove the top 14–20 abundant plasma proteins. However, even with up to 99% of high-abundant proteins removed, most disease biomarkers are in the low ng/mL to pg/mL range. Using label free proteomics, we developed a strategy for the identification and quantification of plasma proteins in control samples. MethodsHeparinised plasma from three healthy volunteers (two women, one man) was obtained. 40 μL plasma was immunodepleted with high performance liquid chromatography (HPLC) Multiple Affinity Removal System (Agilent Technologies, USA). Samples were filtered and buffered exchanged before measurement of protein concentration. Samples were reduced, alkylated, and digested with trypsin overnight. High pH reverse phase HPLC was used to fractionate samples at the peptide level into 85 individual fractions. Each fraction was analysed with ion mobility using Waters G2 high resolution TOF coupled to nanoLC system. Each fraction was analysed with Protein Lynx Global Server software for protein identification and quantification, and Non-Linear Progenesis software for relative protein expression. FindingsAfter fractionation and mass spectrometer analysis, we identified 318 individual proteins that might be potential biomarkers. InterpretationThis method enables more protein biomarkers to be measured compared with unfractionated samples. This method will be used to analyse plasma samples from patients with unresectable pancreatic cancers who receive intravenous omega-3 fatty acids (with a control arm without omega-3 fatty acids). Potential biomarkers identified will be verified with the aim of translating to clinical use for response to treatment. FundingNational Institute for Health Research.
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