bioRxivBiochemistry
Multi-platforms approach for plasma proteomics: complementarity of Olink PEA technology to mass spectrometry-based protein profiling
A. Petrera, C. von Toerne, J. Behler, C. Huth, B. Thorand, A. Hilgendorff, S. M. Hauck
Abstract
The plasma proteome is the ultimate target for biomarker discovery. It stores an endless amount of information on the pathophysiological status of a living organism, which is however still difficult to comprehensively access. The high structural complexity of the plasma proteome can be addressed by either a system-wide and unbiased tool such as mass spectrometry (LC-MS/MS) or a highly sensitive targeted immunoassay such as the Proximity Extension Assays (PEA). In order to address relevant differences and important shared characteristics, we tested the performance of LC-MS/MS in data-dependent and -independent acquisition modes and PEA Olink to measure circulating plasma proteins in 173 human plasma samples from a Southern German population-based cohort. We demonstrated the measurement of more than 300 proteins with both LC-MS/MS approaches applied, mainly including high abundance plasma proteins. By the use of the PEA technology, we measured 728 plasma proteins, covering a broad dynamic range with high sensitivity down to pg/ml concentrations. In a next step, we quantified 35 overlapping proteins with all three analytical platforms, verifying the reproducibility of data distributions, measurement correlation and gender-based differential expression. Our work highlights the limitations and the advantages of both, targeted and untargeted approaches, and prove their complementary strengths. We demonstrated a significant gain in proteome coverage depth and subsequent biological insight by platforms combination - a promising approach for future biomarker and mechanistic studies.
- organism: human
- assay: proteomics
- research focus: benchmarking
- research focus: differential expression
- research focus: protein structure prediction
- approach: experimental assay
- approach: statistical model
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