ArticleAnalytical chemistry2026
Automating Middle-Down Mass Spectrometry Analysis for Extensive Antibody Characterization.
Article in Analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Native Multidimensional Protein Complex Identification and Topology Characterization (Native MudPIT).Analytical chemistry · 2026Article
- Deep Proteoform Sequencing with Top-Down Direct Mass Technology.bioRxiv : the preprint server for biology · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Workflows and software tools for peptide mapping have been extensively optimized and simplified for widespread use. However, top-down and middle-down workflows, despite offering valuable proteoform information not obtainable by peptide mapping, remain demanding in terms of user expertise as well as instrument setup, data analysis, and result validation. Current software offerings have also not been tailored to address targeted protein characterization workflows, often necessitating multiple software tools to accomplish data analysis. To move toward routine intact protein characterization workflows with a single software tool, an automated platform named Proteoform Studio is introduced here. Proteoform Studio features a complete analysis pipeline, allowing a user to set up instrument runs, modify acquisition methods, rapidly deconvolute data to define intact mass features, search results to identify proteoforms, and obtain the highest quality proteoform characterization through automated aggregation of fragmentation results. Additionally, fragment ion matching settings for signal-to-noise ratio and isotope fit score were identified that produced automated matching results well-aligned with manual validation results, thereby reducing the need for time-consuming manual verification. With Proteoform Studio analysis, >80% sequence coverage of light chain (Lc) and the N-terminal portion of heavy chain (Fd) antibody subunits was achieved in an automated fashion using a middle-down approach. These sequence coverage results utilized aggregation of spectral results from different fragmentation methods to take advantage of complementary fragment ion formation. Overall, introducing software to automate antibody subunit analysis will lower barriers to entry and eliminate data analysis bottlenecks, driving adoption in biopharmaceutical assays and ultimately leading to more precise characterization of therapeutic proteoform landscapes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.