Multi-Enzyme Data-Independent Acquisition Mass Spectrometry for Proteoform-Aware Single-shot Proteomics
Biological regulation does not operate at the level of the protein but across ensembles of protein variants shaped by alternative splicing, post-translational modification and endogenous proteolytic processing. Yet most conventional bottom-up discovery proteomics workflows remain optimized for a canonical protein census, leaving much of the sequence-level variation invisible. Multi-enzyme digestion has been recognized as an effective strategy to expand sequence coverage and proteoform sensitivity, but existing implementations rely on resource-heavy per-enzyme liquid chromatography coupled to mass spectrometry (LC-MS) analysis, dedicated measurements for spectral library construction, and semi-manual reconciliation across multiple software tools, restricting the use to specialized laboratories and limited sample cohorts. Here we introduce mE-DIA, a multi-enzyme, proteoform-resolved data independent acquisition (DIA)-MS workflow that multiplexes complementary protease digested samples into a single DIA injection and processes them through a library-free pipeline within a single analysis engine, without supplemental data dependent acquisition (DDA)-MS measurements. The mE-DIA Pipeline graphical user interface orchestrates the analysis, and a dedicated proteoform inference algorithm and the interactive mE-DIA Peptide Mapper viewer make the sequence-level evidence explorable. Comparing the workflow to prevailing DDA-library-based multi-enzyme DIA on the same raw data and within shared search space showed increased identified protein groups by 30 % and protein groups quantified at ≤ 20 % CV by 47 %. A three-enzyme multiplex of the human pathogen Streptococcus pyogenes resolved 990 protein-backbone variants among 309 canonical protein entries, 1.8-fold more than the most productive single-enzyme analysis, including an H69 C-terminal proteoform of the ribosomal protein uS19 that the trypsin-only workflow did not resolve. By consolidating multi-enzyme acquisition, analysis and proteoform interpretation into one accessible toolset, mE-DIA brings sub-gene-resolved evidence within reach of routine single-shot discovery DIA proteomics.
Authors
- Emma Åhrman (ORCID: https://orcid.org/0000-0002-3848-3699)
- Moritz Heusel (ORCID: https://orcid.org/0000-0002-8506-530X)
- Christofer A. Q. Karlsson (ORCID: https://orcid.org/0000-0002-7099-0092)
- Johan A. Malmström (ORCID: https://orcid.org/0000-0002-2889-7169)
- Lotta Happonen (ORCID: https://orcid.org/0000-0002-5922-4549)
Institutions
- Lund University (SE)
Publication Details
- Journal
- bioRxiv (Cold Spring Harbor Laboratory)
- Published
- 2026-11-09
- DOI
- https://doi.org/10.64898/2026.11.06.764873
- Primary Topic
- Advanced Proteomics Techniques and Applications
- Type
- preprint