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SauerMass SpectrometryBottom-up proteomicsData-independent acquisitionBiobankingTranslationSample storageProteomicsLiquid nitrogenBiomarkersClinical researchDia-mshttps://www.ebi.ac.uk/pride/archive/projects/PXD060473Blood SerumSerum sample acquisition and protein digestion were performed following established protocols at the Interdisciplinary Center for Biobanking Lübeck. Blood samples were collected in 9 mL S-Monovettes, cooled at room temperature, and centrifuged. Serum was aliquoted into cryovials and frozen in liquid nitrogen, with a 10 µL sub-aliquot prepared for immediate protein digestion. For protein digestion, 2 µL of thawed serum was diluted and precipitated with acetone. The resulting pellet was resuspended in DOC buffer, reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin overnight. The digestion was quenched with formic acid, centrifuged, and the supernatant was lyophilized and stored at -20°C. High-performance liquid chromatography (HPLC) was performed using a Dionex Ultimate 3000 system with a precolumn and Luna C18(2) analytical column. Two gradient settings were employed: a 90-minute gradient for the pilot study and a proportionally shortened 45-minute gradient for the validation study. The pilot study gradient consisted of a 4-minute sample injection and loading, followed by a linear gradient increasing solvent B (1% formic acid in acetonitrile) from 3% to 25% at 68 minutes, 35% at 73 minutes, and 80% at 80 minutes. This was maintained for 3 minutes, then reduced to 3% over 4 minutes and held for 8 minutes. The validation study gradient followed a similar pattern but was compressed into 45 minutes, with solvent B increasing to 25% at 30.68 minutes, 35% at 32.94 minutes, and 80% at 33.84 minutes until 35.2 minutes. It was then reduced to 3% at 37 minutes and maintained for 8 minutes. Both methods used a flow rate of 5 µl/min. Mass spectrometry analysis was conducted using a TripleTOF 5600+ system with data-independent acquisition-MS in sequential window acquisition of all theoretical mass spectra (SWATH). Key acquisition parameters included an Ion Spray Voltage Floating of 5000 V, ion source gas (GS1) of 15, ion source gas (GS2) of 0, curtain gas at 30, and source temperature set to 0 °C. The optimized declustering potential was 100, collision energy was 19.2, collision energy spread was 5.0, ion release delay was 67, and ion release width was 25. Two 100 precursor isolation window SWATH methods were developed based on the 90- and 45-minute HPLC gradients. Precursor isolation windows were defined using the SWATH Variable Window Calculator V1.1, with a mass range of 350-1250 m/z and window overlap of 1.0 Da. For data-dependent acquisition (DDA), identical instrument parameters were used. MS scans were performed for 350-1250 m/z with an accumulation time of 0.25 seconds, and MS/MS scans for 100-1500 m/z with an accumulation time of 0.05 seconds in high sensitivity mode. This comprehensive methodology allows for detailed analysis of serum samples, enabling the identification and quantification of proteins for biomarker discovery and validation in clinical research.PrideNot availableThe raw SWATH data from both study sub-cohorts were processed using the software tool DIA-NN v1.8.1 with identical settings (5). The software was used in the high-accuracy LC mode with retention time-dependent cross-normalization enabled. Mass accuracy was fixed to 2e-05 (MS2) and 1.5e-05 (MS1), while scan window width was determined automatically. The ‘match between runs’ function was used develop a spectral library using the ‘smart profiling strategy’ from the data-independent acquisition data. The human UniProtKB/swiss-prot database (version 2020/12/6) (6) was used for protein inference from identified peptides. Trypsin/P was specified as protease. The precursor ion generation settings were set to a peptide length of 7–52 amino acids, the maximum number of missed cleavages to one. The maximum number of variable modifications was set to zero. N-terminal methionine excision and cysteine carbamidomethylation were enabled as fixed modifications. The DIA-NN report files were further processed in the DIA-NN R package (5) for MaxLFQ-based protein quantification (7). For the test and validation dataset, a report was generated containing the unique proteins that passed the FDR cut-off of 0.01 applied at the precursor level and were identified and quantified using proteotypic peptides only. A minimum of two unique peptides identified all proteins included in the final datasets. The proteins were mapped to their corresponding gene names; thus, the terms ‘proteins’ and ‘genes’ are used interchangeably in this study. The quantitative data were further pre-processed using the R package DEP (8). The unique protein data sets were filtered for data completeness: each protein had to be identified in at least 70% of samples. A variance stabilizing normalization (9) was applied to the data, including a log2 transformation, before imputing the remaining missing values using the k-nearest neighbour model.ProteomicsProf. Dr.Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Hospital Schleswig-Holstein, Campus LübeckPARTIALHomo Sapiens (human)thorben.sauer@student.uni-luebeck.deNot availableDepartment of Surgery, Laboratory for Surgical Research, University Hospital Schleswig-Holstein, Campus Lübeck, Ratzeburger Allee 160, 23538 Lübeck GermanyGermanyfalseImpact of Single Freeze-Thaw Cycles on Serum Protein Stability: Implications for Clinical Biomarker Validation Using Mass SpectrometryThe validated use of biomarkers for clinical diagnostics and therapy necessitates the availability of a substantial number of high-quality samples, along with a complete set of clinical data. Establishing standards for sample collection, storage, and quality control is essential to reduce the variability of sample quality. This study evaluated the impact of a single freeze-and-thaw (FT) cycle on the quality of liquid nitrogen-stored clinical serum aliquots. This was achieved by data-independent acquisition mass spectrometry (DIA-MS) on a test cohort comprising 25 patients and 99 samples, and a validation cohort comprising 109 patients. Abundance differences of paired samples were assessed by employing biostatistics and bioinformatics approaches, including clustering analyses, linear mixed models, functional annotation, and machine learning. Following the library-free data analysis and implementation of strict data preprocessing, the relative abundance of 213 (pilot study) and 248 (validation study) human serum proteins were analyzed by comparing paired fresh (never frozen) and FT samples. The proteomic data demonstrated high quality and reproducibility. Of 30 target proteins observed in the pilot study, 11 proteins (36.6%) were successfully validated in the validation cohort. These targets exhibited significant changes (q-value <0.05) in measurable protein abundance after one FT cycle. A correlation analysis of the measured protein intensity and storage duration revealed no significant association between the two variables. Of the 11 validated FT-sensitive proteins, CP and IGHV6-1 were identified as potential biomarker candidates for discriminating between malignant and benign pancreatic disease patients. Distinct protein abundance patterns can be discerned between fresh serum samples and samples stored in liquid nitrogen. The findings of this study suggest that FT cycles are important pre-analytical impact factors and should be addressed during the translation of protein-based biomarkers into clinical 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