Introduction Plasma/serum represents an attractive avenue to discover new biomarkers or predict health outcomes associated to any pathophysiological state. However, this is a highly complex mixture that is for example characterized by a large dynamic range in protein concentration with only few highly abundant proteins. Many approaches have been developed, including depletion of high abundance proteins, enrichment of sub-proteomes or fractionation technologies. Data acquisition and processing may also impact plasma/serum proteomics performance. Despite significant improvements in sensitivity and reproducibility, such analysis remains a challenge. We compared several workflows to analyze the serum proteome of brown bears in the most comprehensive way possible, and applied the two best protocols to the comparison of summer-active and hibernating animals. Methods We compared the performance of the in-StageTIP (iST) kit (PreOmics®) for protein digestion in combination or not with high abundance protein depletion (ethanol or perchloric acid) to the new Enrich-iST solution (PreOmics®) and to sample fractionation (AssayMAP Bravo, Agilent) based on High pH Reversed Phase (HpHRP) and Strong Cation eXchange (SCX) chromatography. Samples were analyzed on a nanoElute UHPLC system coupled to a TimsTOF Pro 2 (Bruker) operated in data-dependent acquisition (DDA)–parallel accumulation-serial fragmentation (PASEF) mode and data-independent acquisition PASEF (diaPASEF) mode except for fractionation. For diaPASEF data processing, we evaluated the use of a customized spectral library generated in DDA. The two best-performing protocols were used for label-free quantitative proteomic analysis of serum from summer-active versus winter-hibernating bears (N=8). Preliminary Data Ethanol precipitation of albumin combined with the iST solution led to a slight gain in identification using DDA analyses but hampered DIA analysis by leading to fewer identifications. Similarly, perchloric acid precipitation seemed relevant in DDA analysis with more identified proteins than with the iST solution but opposite results were observed with DIA. The Enrich-iST solution outperformed all other protocols (e.g. +40% more identifications than the iST kit). In any case, the use of DIA (data processing using Spectronaut), compared to DDA (data processing using Mascot and Proline), improved identification performance in terms of the number of identified proteins (up to +65%). Sample fractionation allowed an increase in the number of proteins identified (+200% with HpHRP and +150% with SCX). The DDA analysis of six HphRP fractions led to a greater number of proteins identified compared to one DIA analysis after the use of the iST kit but the use of the Enrich-iST kit remained preferable. Fractionation approaches were used to generate a spectral library and test library-based DIA analysis. Preliminary results seem to indicate a slight gain in protein identification from the hybrid library approach (library-free approach combined with DDA data) compared to library-free or library-based strategy. Overall, based on the latest results, we analyzed the serum of active and hibernating bears using the iST kit combined with a hybrid-library DIA analysis with HpHRP peptide fractionation. A parallel analysis was carried out with the Enrich-iST kit to validate its still unknown performance in terms of quantification. The high performance and complementarity nature of our approaches have provided results that help better understand the physiology of hibernation in bears, and feeds our understanding of how compounds circulating in the serum of hibernating brown bears can protect human muscle cells from atrophy (Chanon et al., 2018, Sci Rep.). Novel Aspect Innovative approaches have improved the quantitative proteomic analysis of bear serum, providing results that can be transposed to the clinic Conflict of Interest Disclosure The authors declare no competing financial interest.