A Highly Automated Shotgun Proteomic Workflow: Clinical Scale and Robustness for Biomarker Discovery in Blood

Abstract

With recent technological developments, protein biomarker discoveries directly from blood have regained interest due to elevated feasibility. Mass spectrometry (MS)-based proteomics can now characterize human plasma proteomes to a greater extent than has ever been possible before. Such deep proteome coverage comes, however, with important limitations in terms of analysis time which is a critical factor in the case of clinical studies. As a consequence, compromises still need to be made to balance the proteome coverage with realistic analysis time frame in clinical research. The analysis of a sufficient number of samples is compulsory to empower statistically robust candidate biomarker findings. We have, therefore, recently developed a scalable automated proteomic pipeline (ASAP²) to enable the proteomic analysis of large numbers of plasma and cerebrospinal fluid (CSF) samples, from dozens to a thousand of samples, with the latter number being currently processed in 15 weeks. A distinct characteristic of ASAP² relies on the possibility to prepare samples in a highly automated way, mostly using 96-well plates. We describe herein a sample preparation procedure for human plasma that includes internal standard spiking, abundant protein removal, buffer exchange, reduction, alkylation, tryptic digestion, isobaric labeling, pooling, and sample purification. Other key elements of the pipeline (i.e., study design, sample tracking, liquid chromatography (LC) tandem MS (MS/MS), data processing, and data analysis) are also highlighted.

Keywords: Automation; Biomarker; Clinical research; Depletion; Human; Isobaric tagging; Large scale; Mass spectrometry; Plasma.