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Ahrman, E.

Publications and source records attributed to Ahrman, E..

2 recordsLinked to original sources

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.

systems biology↗

Inferring the composition of the blood plasma proteome by a human proteome distribution atlas

The plasma proteome is maintained by the influx and efflux of proteins from surrounding organs and cells. To quantify the extent different organs and cells contribute to the plasma proteome composition, we developed a mass spectrometry-based proteomics strategy to infer the origin of proteins detected in human plasma in health and disease. First, we constructed an extensive human proteome atlas from 18 vascularized organs and the most abundant cell types in blood. Second, the atlas was interfaced with previous RNA/protein atlases to objectively define proteome wide protein-organ associations to enable both the inference of origin and the reproducible quantification of organ-specific proteins in plasma. We demonstrate that the resource can determine disease-specific quantitative changes of organ-enriched protein panels in three separate patient cohorts with infection, pancreatitis, and myocardial injury. The strategy can be extended to other diseases to advance our understanding of the processes contributing to plasma proteome dynamics.

systems biology↗