Enhancing plasma proteomics and comparing mass spectrometry with affinity-based technology

Blood is a crucial reservoir of clinically relevant information, necessitating a comprehensive understanding of its protein composition. Exploring plasma proteins is highly competitive within the field of proteomics. However, it faces challenges due to a dynamic range spanning over 10 orders of magnitude. Our objective was to enhance mass spectrometry-based plasma protein identifications using ENRICH-iST (PreOmics) for sample preparation and a timsTOF HT instrument (Bruker) connected to an Ultimate 3000 nano-RSLC (Thermo Fisher Scientific). The acquired data were compared with plasma data from the CLINSPECT-M consortium, originating from various laboratories across Munich, and additionally across platforms using OLINK® Explore 3072 data.

We compared four sample preparation methods: employing ENRICH-iST kit (PreOmics), in solution digest, iST-BCT kit (PreOmics) and perchloric acid precipitation. By applying the ENRICH-iST sample preparation, we increased the protein identifications by about 50%. Gradient length analysis (30min, 70min, and 90min) revealed an approximate 30% increase between 30min and the two longer gradients, with negligible differences between 70min and 90min gradient length. The use of a tailored py_diAID method (Skowronek et al., 2022), using varying window sizes depending on the amount of eluting precursor ions, yielded an additional 10% more protein identification compared to the standard DIA method provided by Bruker.

After selecting the most effective combination, we used plasma ring trail raw data from the CLINSPECT-M consortium to assess the scope of our improved plasma proteomics. We reanalyzed DIA raw files from six different setups, each employing its best practices in plasma processing. DIA_NN was used for data analysis, utilizing a predicted human spectral library to enable comparative evaluations through mpwR (R package for mass spectrometry data, Kardell et al., 2023).

To compare data across platforms, we utilized identical plasma pool samples for measurements on both the OLINK platform and our own. The results revealed distinctiveness for each platform, with approximately 2000 unique protein IDs identified, respectively. The overlap of mass spectrometry and OLINK data was comparably small, with around 850 protein IDs.

In conclusion, our performance in plasma proteomics surpasses the average standards in the field of mass spectrometry. The cross-platform comparison with OLINK indicates an additive effect on protein identifications, potentially enhancing the comprehensiveness of the overall perspective on plasma proteomics.