ArticleNature communications2022
FLASHIda enables intelligent data acquisition for top-down proteomics to boost proteoform identification counts.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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11 citing papers in PubMed.
- The Language of Elution: Autoregressive Prediction of the Next Feature in Untargeted LC-HRMS Lipidomics.ArXiv · 2026Article
- Proton Transfer Charge Reduction Enables Isobaric Labeling-Based Proteoform Quantification of Overlapping Signals in Top-Down Mass Spectrometry.Journal of the American Society for Mass Spectrometry · 2026Article
- Top-Down Mass Spectrometry and Its Current Applications in Biomarker Discovery in Aging and Age-Related Diseases.International journal of molecular sciences · 2026Review
- Top-Down Proteomics: Why and When?Proteomics · 2025Article
- Article
- Top-Down Proteomic Analysis of Limited Samples Using Porous Layer Open Tubular Columns and High-Field Asymmetric Ion Mobility Spectrometry Coupled to Mass Spectrometry.Analytical chemistry · 2025Article
- Cysteine-Directed Isobaric Labeling Combined with GeLC-FAIMS-MS for Quantitative Top-Down Proteomics.Journal of proteome research · 2025Article
- FLASHQuant: A Fast Algorithm for Proteoform Quantification in Top-Down Proteomics.Analytical chemistry · 2024Article
- Mass spectrometry-intensive top-down proteomics: an update on technology advancements and biomedical applications.Analytical methods : advancing methods and applications · 2024Review
- Utilizing Precursor Ion Connectivity of Different Charge States to Improve Peptide and Protein Identification in MS/MS Analysis.Analytical chemistry · 2024Article
- Cysteine Counting via Isotopic Chemical Labeling for Intact Mass Proteoform Identifications in Tissue.Analytical chemistry · 2023Article
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6 authors.
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Abstract
The detailed analysis and structural characterization of proteoforms by top-down proteomics (TDP) has gained a lot of interest in biomedical research. Data-dependent acquisition (DDA) of intact proteins is non-trivial due to the diversity and complexity of proteoforms. Dedicated acquisition methods thus have the potential to greatly improve TDP. Here, we present FLASHIda, an intelligent online data acquisition algorithm for TDP that ensures the real-time selection of high-quality precursors of diverse proteoforms. FLASHIda combines fast charge deconvolution algorithms and machine learning-based quality assessment for optimal precursor selection. In an analysis of E. coli lysate, FLASHIda increases the number of unique proteoform level identifications from 800 to 1500 or generates a near-identical number of identifications in one third of the instrument time when compared to standard DDA mode. Furthermore, FLASHIda enables sensitive mapping of post-translational modifications and detection of chemical adducts. As a software extension module to the instrument, FLASHIda can be readily adopted for TDP studies of complex samples to enhance proteoform identification rates.
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