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Definitive benchmarking of DDA and DIA for host cell protein analysis on the Orbitrap Astral in a regulatory-aligned framework

ABSTRACT: Host cell proteins (HCPs) are critical quality attributes in biotherapeutics that require accurate, specific, and comprehensive quantification. Mass spectrometry (MS)-based workflows are increasingly adopted to overcome the coverage and specificity limitations of immunoassays. This study benchmarks the performance of the Orbitrap Astral mass spectrometer for label-free HCP analysis, comparing data-dependent acquisition (DDA, Top80) and data-independent acquisition (DIA, 4 m/z non-staggered windows) modes. We applied a statistically rigorous framework integrating a stable isotope-labeled HCP mixture for traceable quantification, entrapment-based empirical false discovery proportion estimation, deterministic protein inference, and stratified bootstrapping. Both acquisition modes demonstrated exceptional quantitative fidelity. DIA outperformed DDA in identifications, yielding 45% more proteins and 68% more peptides. Hierarchical Bayesian modeling revealed superior differential linearity in DIA (mean slope 1.0) compared to DDA (slope 0.8). Stratified bootstrap analysis confirmed linearity and accuracy across the dynamic range, with DIA achieving lower limits of quantification (0.6 ppm) versus DDA (1.6 ppm). While both workflows reliably quantified most high-risk HCPs, DIA provided expanded proteome coverage and enhanced fold-change precision. These findings validate the Orbitrap Astral as a high-fidelity platform for HCP analysis in both modes and establish a defensible, regulatory-aligned MS-based framework for routine use in quality control environments.

INSTRUMENT(S): Orbitrap Astral

ORGANISM(S): Cricetulus Griseus (ncbitaxon:10029)

SUBMITTER: Somar Khalil

PROVIDER: MSV000098998 | MassIVE |

SECONDARY ACCESSION(S): PXD067958

REPOSITORIES: MassIVE

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Project description:Data independent acquisition-mass spectrometry (DIA-MS) coupled with liquid chromatography is a promising approach for rapid, automatic sampling of MS/MS data in untargeted metabolomics. However, wide isolation windows in DIA-MS generate MS/MS spectra containing a mixed population of fragment ions together with their precursor ions. This precursor-fragment ion map in a comprehensive MS/MS spectral library is crucial for relative quantification of fragment ions uniquely representative of each precursor ion. However, existing reference libraries are not sufficient for this purpose since the fragmentation patterns of small molecules can vary in different instrument setups. Here we developed a bioinformatics workflow called MetaboDIA to build customized MS/MS spectral libraries using a user's own data dependent acquisition (DDA) data and to perform MS/MS-based quantification with DIA data, thus complementing conventional MS1-based quantification. MetaboDIA also allows users to build a spectral library directly from DIA data in studies of a large sample size. Using a marine algae data set, we show that quantification of fragment ions extracted with a customized MS/MS library can provide as reliable quantitative data as the direct quantification of precursor ions based on MS1 data. To test its applicability in complex samples, we applied MetaboDIA to a clinical serum metabolomics data set, where we built a DDA-based spectral library containing consensus spectra for 1829 compounds. We performed fragment ion quantification using DIA data using this library, yielding sensitive differential expression analysis. </br></br> Serum metabolome of 40 age-related macular degeneration patients and 20 control samples was analyzed using untargeted mass spectrometry. We used data dependent acquisition data to build a MS/MS spectral assay library for more than 1,000 compounds and performed targeted extraction of MS2 ion chromatograms from data independent acquisition analysis.

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Definitive benchmarking of DDA and DIA for host cell protein analysis on the Orbitrap Astral in a regulatory-aligned framework

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