Introduction: Plasma proteomics emerges as a powerful tool for identifying potential off-target effects, unwanted on-target effects and safety concerns associated with novel therapeutic approaches in preclinical studies. Besides the high potential to detect systemic biological changes at an early stage, there are challenges to proteomic plasma analysis. The main limiting factor often displays the large dynamic range of abundance of plasma proteins, which can lead to masking of low abundant proteins, and thus a limited number of proteins that can be identified and quantified. To address this challenge, various methods exist to deplete the high abundant protein fraction, thus enabling deep proteomic profiling. The aim of the study was to apply a dual approach to optimize the proteomic analysis of murine plasma for safety assessment in preclinical animal studies.

Methods: For this purpose, different depletion methods (immunodepletion, selective acid precipitation and ProteoMiner™ protein enrichment technology) were evaluated in comparison to the analysis of neat plasma based on the identification (ID) rates, the number of reproducibly quantified proteins as well as the technical variance and the correlation of protein abundances in depleted and neat plasma. Based on these results, a suitable sample preparation method for preclinical toxicological safety assessment in mice was selected. The second part of the study comprised the optimization of the LC-MS/MS method as well as the generation of a spectral library to further deepen the proteomic analysis. Sample preparation included the depletion of the high abundant protein fraction or dilution of the neat plasma followed by protein digestion using a filter-aided sample preparation protocol. Downstream analysis was performed using a nano-LC system (nanoElute 2, Bruker) online coupled to timsTOF Pro 2 mass spectrometer (Bruker) with parallel accumulation and serial fragmentation. Data were mainly analyzed by library-free as well as library-based searches using FragPipe and DIA-NN computational platforms.

Results: The evaluated depletion methods differ in terms of ID rates, reproducibility and number of exclusively quantifiable proteins. While the highest ID rates were determined with depletion by acid precipitation and ProteoMiner™ protein enrichment technology, the measurement of neat plasma is characterized by its high reproducibility. Optimization of the MS and tims parameters (mass range, ion mobility range and window size as well as sample amount and ramp time) further deepened the proteomic coverage and enabled the identification of more than 800 murine plasma proteins with a 70-minute gradient.

Conclusion: The presented method is suitable for deep profiling of the murine plasma proteome and can be used in preclinical safety assessment studies in future.