Poster Presentation HUPO 2019 - 18th Human Proteome Organization World Congress

Automation of in-solution digestion for large-scale clinical proteomics through Selected Reaction Monitoring Mass Spectrometry (SRM-MS) in a cost-effective way (#641)

Jihyeon Lee 1 2 , Hyunsoo Kim 2 3 , Areum Sohn 1 2 , Injoon Yeo 2 4 , Youngsoo Kim 1 2 3 4
  1. Department of Biomedical Sciences, Seoul National University , Seoul
  2. Seoul National University, Jongro-gu, SEOUL, South Korea
  3. Institute of Medical and Biological Engineering, MRC, Seoul National University, Seoul
  4. Interdisciplinary Program of Bioengineering, Seoul National University , Seoul

Reproducibly quantifying biomarkers in large-scale cohorts remains a challenge in clinical proteomics due to multi-step process for sample preparation which consumes a great deal of time and human labor. Furthermore, many technical variations introduced from the multi-step process should be minimized for reproducible results of clinical assays. The rising demand for making reproducible results of research has led researchers to introduce the robotic liquid handling platform to process of preparation for the protein quantification. However, high cost of consumables for the automatic platform is the biggest obstacle for automation, although it considerably relieves the burden of large scale study. In this study we assessed the reproducibility of automated in-solution digestion while reducing the usage of consumables for automation. The quantification of 26 multiplex assays by Selected reaction monitoring-mass spectrometry (SRM-MS) was conducted in four sets of 24 pooled human serum aliquots. The fixed number of pipette tips (same or fewer than the number of samples) are used to dispense each reagent in each set during digestion process. Reduction of related consumables such as wasted reagents, and reagent stock plates were accompanied by the reduced number of tips for each step. The reproducibility was evaluated by comparing the Coefficient of variations (CVs) values of MS-based quantification data from each set. As a results, the comparable reproducibility could be maintained, while the cost of consumables reduced up to one-sixth of the standard experiment (24 tips used for 24 samples). The reduced cost for automation will enable researchers to facilitate automated workflow for their large-scale clinical research with less cost burden.