Analysis of human blood has long been an area of interest for the discovery of biomarkers which may aid in early diagnosis, and progression of many diseases including cancer.
Serum, which contains the soluble protein components of whole blood, consists of more than 3700 proteins, some of which may be clinically relevant biomarkers. Approximately 1% of the total serum is composed of low-abundance proteins, whereas the remaining 99% is primarily dominated by around 22 high-abundance proteins.
Therefore, depletion of serum samples for proteomics analysis is essential for removing high abundant proteins which otherwise mask the presence of lower-abundant proteins which hold great potential for clinically significant biomarkers of discovery. Within the context of breast cancer, we endeavour to search for biomarkers which are predictive of relapse.
Further robust testing of both established and emerging methods is required to optimize depletion strategies for the ultimate goal of biomarker discovery. In this study, we present a comparative proteomic analysis of serum using antibody-based resin depletion, magnetic nanoparticle enrichment, and dried blood spot depletion, assessed through sensitive mass spectrometry-based label-free proteomics. This work aims to identify the optimal method for characterizing the serum proteome to detect low-abundance proteins in clinically relevant samples.