ArticleJournal of proteome research2023
Definitive Screening Designs to Optimize Library-Free DIA-MS Identification and Quantification of Neuropeptides.
Article in Journal of proteome research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 14 citations in OpenAlex.
- From Screening to Optimization: Strategic Implementation of Design of Experiments (DOE) for Robust Nanoparticle Formulation.Polymers · 2026Review
- Decoding Neuropeptide Complexity: Advancing Neurobiological Insights from Invertebrates to Vertebrates through Evolutionary Perspectives.ACS chemical neuroscience · 2025Review
- MotifQuest: An Automated Pipeline for Motif Database Creation to Improve Peptidomics Database Searching Programs.Journal of the American Society for Mass Spectrometry · 2024Article
- EndoGenius: Optimized Neuropeptide Identification from Mass Spectrometry Datasets.Journal of proteome research · 2024Article
- Restoring T and B cell generation in X-linked severe combined immunodeficiency mice through hematopoietic stem cells adenine base editing.Molecular therapy : the journal of the American Society of Gene Therapy · 2024Article
- Origins, Technological Advancement, and Applications of Peptidomics.Methods in molecular biology (Clifton, N.J.) · 2024Article
- An Updated Guide to the Identification, Quantitation, and Imaging of the Crustacean Neuropeptidome.Methods in molecular biology (Clifton, N.J.) · 2024Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Method optimization is crucial for successful mass spectrometry (MS) analysis. However, extensive method assessments, altering various parameters individually, are rarely performed due to practical limitations regarding time and sample quantity. To maximize sample space for optimization while maintaining reasonable instrumentation requirements, a definitive screening design (DSD) is leveraged for systematic optimization of data-independent acquisition (DIA) parameters to maximize crustacean neuropeptide identifications. While DSDs require several injections, a library-free methodology enables surrogate sample usage for comprehensive optimization of MS parameters to assess biomolecules from limited samples. We identified several parameters contributing significant first- or second-order effects to method performance, and the DSD model predicted ideal values to implement. These increased reproducibility and detection capabilities enabled the identification of 461 peptides, compared to 375 and 262 peptides identified through data-dependent acquisition (DDA) and a published DIA method for crustacean neuropeptides, respectively. Herein, we demonstrate a DSD optimization workflow, using standard material, not reliant on spectral libraries for the analysis of any low abundance molecules from previous samples of limited availability. This extends the DIA method to low abundance isoforms dysregulated or only detectable in disease samples, thus improving characterization of previously inaccessible biomolecules, such as neuropeptides. Data are available via ProteomeXchange with identifier PXD038520.
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Registered trials
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