Prideapplication/xmlftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/checksum.txtftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/uniprotkb_taxonomy_id_9606_AND_reviewed_2025_02_19.fastaftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/ReproSilSaphir100_C18_1p5um.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/DIANN_reports.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/Raw_files_Reprospher100_PhenylHexyl_1p8um.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/Raw_files_ExsilMono100_C18_1p35um.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/Raw_files_ReproSilSaphir100_C18_1p3um.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/Raw_files_Reprospher100_C8_1p9um.zipftp://ftp.pride.ebi.ac.uk/pride/data/archive/2026/06/PXD067710/human_reviewed_isoforms_fasta_diann_speclib_Ox_AcN_182beta.predicted.speclibprimary200OKjomueller@biochem.mpg.deMario OroshiMass SpectrometryBottom-up proteomicsChromatographic selectivityMethod developmentOrbitrap astralLc-ms proteomicsColumn performancehttps://www.ebi.ac.uk/pride/archive/projects/PXD067710Hela CellHeLa cell cultivation HeLa cells (S3 subclone, ATCC) were maintained in Dulbecco's modified Eagle's medium supplemented with 10% fetal bovine serum, 20 mM glutamine, and 1% penicillin-streptomycin at 37°C in a humidified 5% CO2 atmosphere. Mycoplasma testing was performed regularly to ensure culture sterility. Cells were grown to approximately 80% confluence before harvesting. Following trypsinization with 0.25% trypsin/EDTA solution, cells were collected and transferred to 15 mL conical tubes. Cell pellets were washed twice using cold Tris-buffered saline (TBS) and recovered by centrifugation at 200g for 10 minutes. After removing the supernatant, cell pellets were immediately frozen in liquid nitrogen and stored at -80°C for subsequent analysis. Cell lysate preparation Cell pellets were lysed using the PREOMICS lysis buffer (PreOmics, Martinsired, Germany). The lysis protocol involved heating samples at 95°C for 15 minutes with continuous agitation at 1,500 rpm, followed by probe sonication using short pulses (10 cycles of 5 seconds on/off at 20% amplitude). Cellular debris was removed by high-speed centrifugation for 5 minutes. Protein quantification was performed using tryptophan fluorescence measurement. For proteolytic digestion, both LysC and trypsin were added at a 1:100 enzyme-to-protein ratio (w/w) and incubated overnight at 37°C with gentle agitation at 1,500 rpm. The digestion reaction was terminated by acidification with 1% trifluoroacetic acid (TFA). Peptides were purified using styrene-divinylbenzene-reversed phase sulfonate (SDB-RPS) solid-phase extraction and reconstituted in 0.1% formic acid with 2% acetonitrile for LC-MS/MS analysis.PrideData analysis / Spectral search Raw MS files were converted to mzML format using ThermoRawFileParser (v4.3, Thermo Fisher Scientific) with format and metadata parameters set to 1 and 0, respectively. File conversion was performed in parallel on a high-performance computing cluster for efficient processing. Spectral searches were conducted using DIA-NN (version 2.0) (Demichev et al, 2020) against the UniProt human Swiss-Prot reference proteome database including isoforms (version 19.02.2025). An in-silico spectral library was generated from the FASTA sequences incorporating oxidation of methionine and N-terminal acetylation as variable modifications. Trypsin specificity was set with up to 2 missed cleavages allowed. Mass accuracy tolerances were set to 10 ppm for both MS1 and MS2 levels, with a scan window parameter of 7. Search parameters included match-between-runs functionality using the "--usequant" and "--reanalyse" flags, along with peak centering, smart profiling, retention time profiling, and relaxed protein inference settings. False discovery rate control was applied at 1% at the peptide-spectrum match level using a target-decoy approach. Protein quantification incorporated both MS1 and MS2 data without interference signal removal. Bioinformatics analysis The 'Report.parquet' output files from DIA-NN were used for all downstream analyses. All data processing and analysis steps were performed in the Python programming environment (v.3.12.10) using custom analysis scripts. Data processing was conducted using pandas (v2.2.3) for manipulation of proteomics data matrices and NumPy (v2.2.5) for numerical operations, while visualization and statistical analyses employed SciPy (v1.15.3), matplotlib (v3.10.3), and seaborn (v0.13.2). Although four technical replicates were measured for each column condition, only three replicates were included in the final analysis, with the first run of each column systematically excluded from the dataset. Visualization of raw files was conducted using the alpharaw package (MannLabs/alpharaw, 2025).ProteomicsDr. Johannes Bruno Müller-ReifDept. of Proteomics and Signal Transduction Max Planck Institute of BiochemistryPARTIALHomo Sapiens (human)oroshi@biochem.mpg.deNot availableProteomicsGermanyfalseLimited impact of column chemistry and length on proteome coverage under high-speed DIAThe evolution of mass spectrometry-based proteomics has been driven by continuous advances in instrumentation, sample preparation, and data acquisition strategies. While chromatographic separation has historically been considered a critical bottleneck in achieving comprehensive proteome coverage, recent developments in ultra-fast mass spectrometry acquisition fundamentally challenge this paradigm. We investigated whether traditional chromatographic optimization principles established during the early era of proteomics remain essential in contemporary workflows. Using five distinct stationary phases, C18 chemistries, C8, and Phenyl-Hexyl, across eight column lengths (40-140 mm), we evaluated proteome identification performance using state-of-the-art data-independent acquisition on the Orbitrap Astral mass spectrometer with HeLa tryptic digests. Despite substantial chromatographic differences in selectivity and peak characteristics that would have profoundly influenced analytical outcomes in earlier instrumentation generations, we observed remarkably convergent proteome coverage metrics. All C18 and C8 phases consistently achieved over 150,000 precursor and approximately 9,000 protein group identifications, regardless of column length variations. While distinct selectivity fingerprints persisted across chemistries, these chromatographic differences did not translate into meaningful variations in bulk identification depth under high-speed acquisition conditions exceeding 200 Hz. From these findings we conclude that the analytical bottleneck has fundamentally shifted from chromatographic resolution to mass spectrometric sampling efficiency, where comprehensive peptide identification is now achieved through advanced spectral deconvolution rather than physical separation alone. This paradigmatic shift suggests that method development priorities in modern proteomics should evolve beyond traditional separation optimization to emphasize operational robustness, analytical throughput, and systematic reproducibility for routine 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